03082nas a2200313   4500008004100000022001400041245013800055210006900193260001600262490000700278520206400285653001002349653002102359653002502380653002702405653003702432653001102469653001102480653000902491653001702500653001602517653003102533653002502564653001602589100001702605700002102622700001302643856011202656       2026                        eng d  a1741-255200aMethodological optimization for eliciting robust median nerve somatosensory evoked potentials for realtime single trial applications.0 aMethodological optimization for eliciting robust median nerve so  c2026 Jan 090 v233 a<p>Single-trial measurement of median nerve somatosensory evoked potentials (SEPs) with noninvasive electroencephalography (EEG) is challenging due to low signal-to-noise ratio (SNR), limiting its use in real-time neurorehabilitation applications. We describe and evaluate methodological optimizations for eliciting reliable median nerve SEPs measurable in real time, with reduced reliance on post-processing.In twelve healthy participants, two sessions each, SEPs were assessed at three pulse widths (0.1, 0.5, 1 ms), at a low-frequency stimulation (0.5 Hz ± 10%), and at an intensity sufficient to evoke consistent and robust sensory nerve action potentials and compound muscle action potentials. The evoked potential operant conditioning system platform was used to monitor responses in real time. Feasibility was also evaluated in a participant with incomplete spinal cord injury (iSCI).SEP P50 and N70 were reliably elicited in healthy participants, and in individual with iSCI, across all tested pulse widths with minimal discomfort. N70 amplitude increased significantly with pulse width (χ2= 17.64,= 0.0001,= 0.80), while P50 amplitude remained unchanged. SNR showed a significant pulse width-dependent increase (χ2= 7.82,= 0.02,= 0.35) with improvements of 40% and 52% at 0.5 and 1 ms, respectively. N70 single-trial separability significantly improved at 1 ms (AUC of 0.83,χ2= 8.17,= 0.017), including the iSCI participant (0.84-less impaired hand, 0.79-more impaired hand). Test-retest reliability (intraclass correlation coefficient = 0.70-0.84,< 0.05) was highest at 0.5 ms, indicating more consistent N70 and P50 measurements across sessions at a longer pulse width.Robust median nerve SEPs can be measured at single trials with methodological optimizations such as a longer pulse width (0.5-1 ms), low frequency (0.5 Hz), a consistent afferent excitation guided by nerve and muscle responses, and a robust EEG acquisition system. This setup can be useful for real time SEP-based brain computer interface applications for rehabilitation.</p>10aAdult10aComputer Systems10aElectric Stimulation10aElectroencephalography10aEvoked Potentials, Somatosensory10aFemale10aHumans10aMale10aMedian Nerve10aMiddle Aged10aReproducibility of Results10aSpinal Cord Injuries10aYoung Adult1 aGupta, Disha1 aBrangaccio, Jodi1 aHill, NJ  uhttps://www.neurotechcenter.org/publications/2026/methodological-optimization-eliciting-robust-median-nerve04347nas a2200433   4500008004100000022001400041245015100055210006900206260001600275490000700291520297600298653001003274653000903284653001903293653002403312653001103336653001503347653001103362653000903373653001603382653002803398653004603426653002803472100001803500700002303518700002103541700002203562700001803584700001403602700001903616700001703635700002603652700002403678700002103702700002303723700001903746700002303765856012503788       2025                        eng d  a2050-084X00aThe effect of transcutaneous auricular vagus nerve stimulation on cardiovascular function in subarachnoid hemorrhage patients: A randomized trial.0 aeffect of transcutaneous auricular vagus nerve stimulation on ca  c2025 Jan 090 v133 a<p><b>BACKGROUND: </b>Subarachnoid hemorrhage (SAH) is characterized by intense central inflammation, leading to substantial post-hemorrhagic complications such as vasospasm and delayed cerebral ischemia. Given the anti-inflammatory effect of transcutaneous auricular vagus nerve stimulation (taVNS) and its ability to promote brain plasticity, taVNS has emerged as a promising therapeutic option for SAH patients. However, the effects of taVNS on cardiovascular dynamics in critically ill patients, like those with SAH, have not yet been investigated. Given the association between cardiac complications and elevated risk of poor clinical outcomes after SAH, it is essential to characterize the cardiovascular effects of taVNS to ensure this approach is safe in this fragile population. Therefore, this study assessed the impact of both acute and repetitive taVNS on cardiovascular function.</p><p><b>METHODS: </b>In this randomized clinical trial, 24 SAH patients were assigned to either a taVNS treatment or a sham treatment group. During their stay in the intensive care unit, we monitored patient electrocardiogram readings and vital signs. We compared long-term changes in heart rate, heart rate variability (HRV), QT interval, and blood pressure between the two groups. Additionally, we assessed the effects of acute taVNS by comparing cardiovascular metrics before, during, and after the intervention. We also explored acute cardiovascular biomarkers in patients exhibiting clinical improvement.</p><p><b>RESULTS: </b>We found that repetitive taVNS did not significantly alter heart rate, QT interval, blood pressure, or intracranial pressure (ICP). However, repetitive taVNS increased overall HRV and parasympathetic activity compared to the sham treatment. The increase in parasympathetic activity was most pronounced from 2 to 4 days after initial treatment (Cohen's  = 0.50). Acutely, taVNS increased heart rate, blood pressure, and peripheral perfusion index without affecting the corrected QT interval, ICP, or HRV. The acute post-treatment elevation in heart rate was more pronounced in patients who experienced a decrease of more than one point in their modified Rankin Score at the time of discharge.</p><p><b>CONCLUSIONS: </b>Our study found that taVNS treatment did not induce adverse cardiovascular effects, such as bradycardia or QT prolongation, supporting its development as a safe immunomodulatory treatment approach for SAH patients. The observed acute increase in heart rate after taVNS treatment may serve as a biomarker for SAH patients who could derive greater benefit from this treatment.</p><p><b>FUNDING: </b>The American Association of Neurological Surgeons (ALH), The Aneurysm and AVM Foundation (ALH), The National Institutes of Health R01-EB026439, P41-EB018783, U24-NS109103, R21-NS128307 (ECL, PB), McDonnell Center for Systems Neuroscience (ECL, PB), and Fondazione Neurone (PB).</p><p><b>CLINICAL TRIAL NUMBER: </b>NCT04557618.</p>10aAdult10aAged10aBlood Pressure10aElectrocardiography10aFemale10aHeart Rate10aHumans10aMale10aMiddle Aged10aSubarachnoid Hemorrhage10aTranscutaneous Electric Nerve Stimulation10aVagus Nerve Stimulation1 aTan, Gansheng1 aHuguenard, Anna, L1 aDonovan, Kara, M1 aDemarest, Phillip1 aLiu, Xiaoxuan1 aLi, Ziwei1 aAdamek, Markus1 aLavine, Kory1 aVellimana, Ananthv, K1 aKummer, Terrance, T1 aOsbun, Joshua, W1 aZipfel, Gregory, J1 aBrunner, Peter1 aLeuthardt, Eric, C  uhttps://www.neurotechcenter.org/publications/2025/effect-transcutaneous-auricular-vagus-nerve-stimulation-cardiovascular01994nas a2200277   4500008004100000022001400041245005800055210005400113260001600167300000700183490000700190520119700197653001301394653001301407653001601420653001101436653001201447653003201459653002801491100001601519700001901535700002701554700001901581700001501600856010101615       2025                        eng d  a2052-446300aA human single-neuron dataset for object recognition.0 ahuman singleneuron dataset for object recognition  c2025 Jan 15  a790 v123 a<p>Object recognition is fundamental to how we interact with and interpret the world around us. The human amygdala and hippocampus play a key role in object recognition, contributing to both the encoding and retrieval of visual information. Here, we recorded single-neuron activity from the human amygdala and hippocampus when neurosurgical epilepsy patients performed a one-back task using naturalistic object stimuli. We employed two sets of naturalistic object images from leading datasets extensively used in primate neural recordings and computer vision models: we recorded 1204 neurons using the ImageNet stimuli, which included broader object categories (10 different images per category for 50 categories), and we recorded 512 neurons using the Microsoft COCO stimuli, which featured a higher number of images per category (50 different images per category for 10 categories). Together, our extensive dataset, offering the highest spatial and temporal resolution currently available in humans, will not only facilitate a comprehensive analysis of the neural correlates of object recognition but also provide valuable opportunities for training and validating computational models.</p>10aAmygdala10aEpilepsy10aHippocampus10aHumans10aNeurons10aPattern Recognition, Visual10aRecognition, Psychology1 aCao, Runnan1 aBrunner, Peter1 aBrandmeir, Nicholas, J1 aWillie, Jon, T1 aWang, Shuo  uhttps://www.neurotechcenter.org/publications/2025/human-single-neuron-dataset-object-recognition03376nas a2200409   4500008004100000022001400041245011600055210006900171260001300240300001000253490000700263520216200270653001002432653000902442653003002451653002002481653002702501653001102528653001702539653001102556653000902567653001602576653001902592653001202611653002402623653002502647653001102672653002602683653001702709653002002726100001902746700002002765700002002785700002202805700002302827856011602850       2025                        eng d  a1552-684400aIpsiHand Brain-Computer Interface Therapy Induces Broad Upper Extremity Motor Rehabilitation in Chronic Stroke.0 aIpsiHand BrainComputer Interface Therapy Induces Broad Upper Ext  c2025 Jan  a74-860 v393 a<p><b>BACKGROUND: </b>Chronic hemiparetic stroke patients have very limited benefits from current therapies. Brain-computer interface (BCI) engaging the unaffected hemisphere has emerged as a promising novel therapeutic approach for chronic stroke rehabilitation.</p><p><b>OBJECTIVES: </b>This study investigated the effectiveness of contralesionally-controlled BCI therapy in chronic stroke patients with impaired upper extremity motor function. We further explored neurophysiological features of motor recovery driven by BCI. We hypothesized that BCI therapy would induce a broad motor recovery in the upper extremity, and there would be corresponding changes in baseline theta and gamma oscillations, which have been shown to be associated with motor recovery.</p><p><b>METHODS: </b>Twenty-six prospectively enrolled chronic hemiparetic stroke patients performed a therapeutic BCI task for 12 weeks. Motor function assessment data and resting state electroencephalogram signals were acquired before initiating BCI therapy and across BCI therapy sessions. The Upper Extremity Fugl-Meyer assessment served as a primary motor outcome assessment tool. Theta-gamma cross-frequency coupling (CFC) was computed and correlated with motor recovery.</p><p><b>RESULTS: </b>Chronic stroke patients achieved significant motor improvement in both proximal and distal upper extremity with BCI therapy. Motor function improvement was independent of Botox application. Theta-gamma CFC enhanced bilaterally over the C3/C4 motor electrodes and positively correlated with motor recovery across BCI therapy sessions.</p><p><b>CONCLUSIONS: </b>BCI therapy resulted in significant motor function improvement across the proximal and distal upper extremities of patients, which significantly correlated with theta-gamma CFC increases in the motor regions. This may represent rhythm-specific cortical oscillatory mechanism for BCI-driven rehabilitation in chronic stroke patients.</p><p><b>TRIAL REGISTRATION: </b>Advarra Study: https://classic.clinicaltrials.gov/ct2/show/NCT04338971 and Washington University Study: https://classic.clinicaltrials.gov/ct2/show/NCT03611855.</p>10aAdult10aAged10abrain-computer interfaces10aChronic Disease10aElectroencephalography10aFemale10aGamma Rhythm10aHumans10aMale10aMiddle Aged10aMotor Activity10aParesis10aProspective Studies10aRecovery of Function10aStroke10aStroke Rehabilitation10aTheta Rhythm10aUpper Extremity1 aRustamov, Nabi1 aSouders, Lauren1 aSheehan, Lauren1 aCarter, Alexandre1 aLeuthardt, Eric, C  uhttps://www.neurotechcenter.org/publications/2025/ipsihand-brain-computer-interface-therapy-induces-broad-upper03432nas a2200361   4500008004100000022001400041245014400055210006900199260001300268300001100281490000800292520223300300653001002533653002002543653001502563653002602578653002702604653001102631653003102642100003102673700002702704700002002731700001702751700002102768700001902789700001902808700002302827700002602850700002702876700001802903700002602921856012302947       2024                        eng d  a1872-678X00aAnatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods.0 aAnatomical registration of intracranial electrodes Robust modelb  c2024 Apr  a1100560 v4043 a<p><b>BACKGROUND: </b>Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.</p><p><b>NEW METHODS: </b>We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes' CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.</p><p><b>RESULTS: </b>We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with < 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.</p><p><b>COMPARISON WITH EXISTING METHODS: </b>GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.</p><p><b>CONCLUSION: </b>GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.</p>10aBrain10aCerebral Cortex10aElectrodes10aElectrodes, Implanted10aElectroencephalography10aHumans10aMagnetic Resonance Imaging1 aBlenkmann, Alejandro, Omar1 aLeske, Sabine, Liliana1 aLlorens, Anaïs1 aLin, Jack, J1 aChang, Edward, F1 aBrunner, Peter1 aSchalk, Gerwin1 aIvanovic, Jugoslav1 aLarsson, Pål, Gunnar1 aKnight, Robert, Thomas1 aEndestad, Tor1 aSolbakk, Anne-Kristin  uhttps://www.neurotechcenter.org/publications/2024/anatomical-registration-intracranial-electrodes-robust-model-based-003070nas a2200301   4500008004100000022001400041245007000055210006700125260001600192300001100208490000700219520218100226653001002407653001002417653001502427653002502442653001202467653001102479653003102490653001402521653000902535100002102544700001702565700002302582700002402605700002302629856011602652       2024                        eng d  a1097-019300aComplexity organization of resting-state functional-MRI networks.0 aComplexity organization of restingstate functionalMRI networks  c2024 Aug 15  ae268090 v453 a<p>Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.</p>10aAdult10aBrain10aConnectome10aDefault Mode Network10aEntropy10aHumans10aMagnetic Resonance Imaging10aNerve Net10aRest1 aTrevino, Gabriel1 aLee, John, J1 aShimony, Joshua, S1 aLuckett, Patrick, H1 aLeuthardt, Eric, C  uhttps://www.neurotechcenter.org/publications/2024/complexity-organization-resting-state-functional-mri-networks03030nas a2200361   4500008004100000022001400041245013300055210006900188260001700257300001200274490000700286520191700293653001002210653001102220653002702231653001102258653000902269653002302278653001002301653001602311653002802327653001402355653001602369100001802385700001602403700001802419700002202437700001902459700001902478700002402497700002302521856012402544       2024                        eng d  a1876-475400aDoes vibrotactile stimulation of the auricular vagus nerve enhance working memory? A behavioral and physiological investigation.0 aDoes vibrotactile stimulation of the auricular vagus nerve enhan  c2024 Mar-Apr  a460-4680 v173 a<p><b>BACKGROUND: </b>Working memory is essential to a wide range of cognitive functions and activities. Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising method to improve working memory performance. However, the feasibility and scalability of electrical stimulation are constrained by several limitations, such as auricular discomfort and inconsistent electrical contact.</p><p><b>OBJECTIVE: </b>We aimed to develop a novel and practical method, vibrotactile taVNS, to improve working memory. Further, we investigated its effects on arousal, measured by skin conductance and pupil diameter.</p><p><b>METHOD: </b>This study included 20 healthy participants. Behavioral response, skin conductance, and eye tracking data were concurrently recorded while the participants performed N-back tasks under three conditions: vibrotactile taVNS delivered to the cymba concha, earlobe (sham control), and no stimulation (baseline control).</p><p><b>RESULTS: </b>In 4-back tasks, which demand maximal working memory capacity, active vibrotactile taVNS significantly improved the performance metric d compared to the baseline but not to the sham. Moreover, we found that the reduction rate of d with increasing task difficulty was significantly smaller during vibrotactile taVNS sessions than in both baseline and sham conditions. Arousal, measured as skin conductance and pupil diameter, declined over the course of the tasks. Vibrotactile taVNS rescued this arousal decline, leading to arousal levels corresponding to optimal working memory levels. Moreover, pupil diameter and skin conductance level were higher during high-cognitive-load tasks when vibrotactile taVNS was delivered to the concha compared to baseline and sham.</p><p><b>CONCLUSION: </b>Our findings suggest that vibrotactile taVNS modulates the arousal pathway and could be a potential intervention for enhancing working memory.</p>10aAdult10aFemale10aGalvanic Skin Response10aHumans10aMale10aMemory, Short-Term10aPupil10aVagus Nerve10aVagus Nerve Stimulation10aVibration10aYoung Adult1 aTan, Gansheng1 aAdams, Josh1 aDonovan, Kara1 aDemarest, Phillip1 aWillie, Jon, T1 aBrunner, Peter1 aGorlewicz, Jenna, L1 aLeuthardt, Eric, C  uhttps://www.neurotechcenter.org/publications/2024/does-vibrotactile-stimulation-auricular-vagus-nerve-enhance-working-001915nas a2200349   4500008004100000022001400041245004100055210004000096260001600136300000900152490000700161520102100168653001001189653001001199653002001209653002001229653001101249653001101260653000901271653001801280653001601298653001601314100001301330700001901343700001601362700002201378700002501400700001901425700001901444700001701463856008501480       2024                        eng d  a2041-172300aGraded decisions in the human brain.0 aGraded decisions in the human brain  c2024 May 21  a43080 v153 a<p>Decision-makers objectively commit to a definitive choice, yet at the subjective level, human decisions appear to be associated with a degree of uncertainty. Whether decisions are definitive (i.e., concluding in all-or-none choices), or whether the underlying representations are graded, remains unclear. To answer this question, we recorded intracranial neural signals directly from the brain while human subjects made perceptual decisions. The recordings revealed that broadband gamma activity reflecting each individual's decision-making process, ramped up gradually while being graded by the accumulated decision evidence. Crucially, this grading effect persisted throughout the decision process without ever reaching a definite bound at the time of choice. This effect was most prominent in the parietal cortex, a brain region traditionally implicated in decision-making. These results provide neural evidence for a graded decision process in humans and an analog framework for flexible choice behavior.</p>10aAdult10aBrain10aChoice Behavior10aDecision Making10aFemale10aHumans10aMale10aParietal Lobe10aUncertainty10aYoung Adult1 aXie, Tao1 aAdamek, Markus1 aCho, Hohyun1 aAdamo, Matthew, A1 aRitaccio, Anthony, L1 aWillie, Jon, T1 aBrunner, Peter1 aKubanek, Jan  uhttps://www.neurotechcenter.org/publications/2024/graded-decisions-human-brain-002375nas a2200361   4500008004100000022001400041245008900055210006900144260001600213300000900229490000700238520131500245653001001560653001201570653001001582653001801592653002701610653000901637653001501646653001101661653001201672100002101684700002101705700001801726700002201744700002401766700002201790700002001812700001901832700002101851700001901872856012201891       2024                        eng d  a2045-232200aMotor imagery ability scores are related to cortical activation during gait imagery.0 aMotor imagery ability scores are related to cortical activation   c2024 Mar 03  a52070 v143 a<p>Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the θ, α, and β band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson's and Spearman's coefficients to correlate MI ability scores and average ERD power (avgERD). Positive correlations were identified between VMIQ and avgERD of the middle cingulum in the β band and with avgERD of the left insula, right precentral area, and right middle occipital region in the θ band. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.</p>10aAdult10aAnimals10aBrain10aCell Membrane10aElectroencephalography10aGait10aGastropoda10aHumans10aWalking1 aPutzolu, Martina1 aSamogin, Jessica1 aBonassi, Gaia1 aCosentino, Carola1 aMezzarobba, Susanna1 aBotta, Alessandro1 aAvanzino, Laura1 aMantini, Dante1 aVato, Alessandro1 aPelosin, Elisa  uhttps://www.neurotechcenter.org/publications/2024/motor-imagery-ability-scores-are-related-cortical-activation-during02169nas a2200301   4500008004100000022001400041245015400055210006900209260001300278300000800291490000900299520119500308653000901503653002101512653001101533653001101544653000901555653001601564653001301580653001101593653002601604100002701630700001901657700002301676700002801699700001701727856012301744       2024                        eng d  a2694-060400aMultidimensional feature analysis shows stratification in robotic-motor-training gains based on the level of pre-training motor impairment in stroke.0 aMultidimensional feature analysis shows stratification in roboti  c2024 Jul  a1-50 v20243 a<p>Stroke involves heterogeneity in injury and ongoing endogenous recovery, which are seldom stratified before testing post-stroke robot assisted motor training (RAMT). Pretraining variations, especially sensory-motor differences may also affect the gains achieved from the RAMT. Moreover, one assessment test may not effectively characterize the baseline sensory-motor status or the RAMT gains. Pre-therapy stratification may help personalize therapy and increase therapy gains. Towards this goal, we propose a data-driven approach to assess multiple functional scores with t-distributed stochastic neighbor embedding and affinity propagation clustering, both for pre-therapy and RAMT gains. Data included behavioral scores from 27 people with chronic stroke who underwent RAMT for finger movement. Three clusters were observed at start-of-therapy (SoT), concurrent with the overall impairment level. Four clusters were observed for the RAMT gains, indicating specific improvements. The SoT clusters showed agreement with the RAMT gain clusters, suggesting that the pre-therapy state, assessed across multiple domains, could be useful in guiding RAMT interventions to improve outcomes.</p>10aAged10aCluster Analysis10aFemale10aHumans10aMale10aMiddle Aged10aRobotics10aStroke10aStroke Rehabilitation1 aRueda-Parra, Sebastian1 aPerry, Joel, C1 aWolbrecht, Eric, T1 aReinkensmeyer, David, J1 aGupta, Disha  uhttps://www.neurotechcenter.org/publications/2024/multidimensional-feature-analysis-shows-stratification-robotic-motor02594nas a2200325   4500008004100000022001400041245010900055210006900164260001600233300000900249490000700258520156700265653001001832653002301842653002701865653002401892653001301916653001101929653001801940100002101958700001801979700001601997700001902013700002502032700002102057700002302078700001902101700002502120856012302145       2024                        eng d  a2041-172300aMultisensory flicker modulates widespread brain networks and reduces interictal epileptiform discharges.0 aMultisensory flicker modulates widespread brain networks and red  c2024 Apr 11  a31560 v153 a<p>Modulating brain oscillations has strong therapeutic potential. Interventions that both non-invasively modulate deep brain structures and are practical for chronic daily home use are desirable for a variety of therapeutic applications. Repetitive audio-visual stimulation, or sensory flicker, is an accessible approach that modulates hippocampus in mice, but its effects in humans are poorly defined. We therefore quantified the neurophysiological effects of flicker with high spatiotemporal resolution in patients with focal epilepsy who underwent intracranial seizure monitoring. In this interventional trial (NCT04188834) with a cross-over design, subjects underwent different frequencies of flicker stimulation in the same recording session with the effect of sensory flicker exposure on local field potential (LFP) power and interictal epileptiform discharges (IEDs) as primary and secondary outcomes, respectively. Flicker focally modulated local field potentials in expected canonical sensory cortices but also in the medial temporal lobe and prefrontal cortex, likely via resonance of stimulated long-range circuits. Moreover, flicker decreased interictal epileptiform discharges, a pathological biomarker of epilepsy and degenerative diseases, most strongly in regions where potentials were flicker-modulated, especially the visual cortex and medial temporal lobe. This trial met the scientific goal and is now closed. Our findings reveal how multi-sensory stimulation may modulate cortical structures to mitigate pathological activity in humans.</p>10aBrain10aCross-Over Studies10aElectroencephalography10aEpilepsies, Partial10aEpilepsy10aHumans10aTemporal Lobe1 aBlanpain, Lou, T1 aCole, Eric, R1 aChen, Emily1 aPark, James, K1 aWalelign, Michael, Y1 aGross, Robert, E1 aCabaniss, Brian, T1 aWillie, Jon, T1 aSinger, Annabelle, C  uhttps://www.neurotechcenter.org/publications/2024/multisensory-flicker-modulates-widespread-brain-networks-and-reduces02153nas a2200313   4500008004100000022001400041245009400055210006900149260001600218300001100234490000700245520118200252653001301434653002301447653001601470653001101486653001301497653003201510653002801542100001601570700001601586700001901602700001901621700001201640700002201652700002701674700001501701856012301716       2024                        eng d  a2211-124700aNeural mechanisms of face familiarity and learning in the human amygdala and hippocampus.0 aNeural mechanisms of face familiarity and learning in the human   c2024 Jan 23  a1135200 v433 a<p>Recognizing familiar faces and learning new faces play an important role in social cognition. However, the underlying neural computational mechanisms remain unclear. Here, we record from single neurons in the human amygdala and hippocampus and find a greater neuronal representational distance between pairs of familiar faces than unfamiliar faces, suggesting that neural representations for familiar faces are more distinct. Representational distance increases with exposures to the same identity, suggesting that neural face representations are sharpened with learning and familiarization. Furthermore, representational distance is positively correlated with visual dissimilarity between faces, and exposure to visually similar faces increases representational distance, thus sharpening neural representations. Finally, we construct a computational model that demonstrates an increase in the representational distance of artificial units with training. Together, our results suggest that the neuronal population geometry, quantified by the representational distance, encodes face familiarity, similarity, and learning, forming the basis of face recognition and memory.</p>10aAmygdala10aFacial Recognition10aHippocampus10aHumans10aLearning10aPattern Recognition, Visual10aRecognition, Psychology1 aCao, Runnan1 aWang, Jinge1 aBrunner, Peter1 aWillie, Jon, T1 aLi, Xin1 aRutishauser, Ueli1 aBrandmeir, Nicholas, J1 aWang, Shuo  uhttps://www.neurotechcenter.org/publications/2024/neural-mechanisms-face-familiarity-and-learning-human-amygdala-and-004496nas a2200409   4500008004100000022001400041245016200055210006900217260000900286300001300295490000700308520319000315653001003505653001103515653001103526653001703537653000903554653001603563653002403579653004203603653002803645653002203673653002803695100002003723700001803743700002303761700001903784700001803803700002103821700001803842700002203860700002003882700002003902700001903922700002003941856012503961       2024                        eng d  a1932-620300aNon-invasive Auricular Vagus nerve stimulation for Subarachnoid Hemorrhage (NAVSaH): Protocol for a prospective, triple-blinded, randomized controlled trial.0 aNoninvasive Auricular Vagus nerve stimulation for Subarachnoid H  c2024  ae03011540 v193 a<p><b>BACKGROUND: </b>Inflammation has been implicated in driving the morbidity associated with subarachnoid hemorrhage (SAH). Despite understanding the important role of inflammation in morbidity following SAH, there is no current effective way to modulate this deleterious response. There is a critical need for a novel approach to immunomodulation that can be safely, rapidly, and effectively deployed in SAH patients. Vagus nerve stimulation (VNS) provides a non-pharmacologic approach to immunomodulation, with prior studies demonstrating VNS can reduce systemic inflammatory markers, and VNS has had early success treating inflammatory conditions such as arthritis, sepsis, and inflammatory bowel diseases. The aim of the Non-invasive Auricular Vagus nerve stimulation for Subarachnoid Hemorrhage (NAVSaH) trial is to translate the use of non-invasive transcutaneous auricular VNS (taVNS) to spontaneous SAH, with our central hypothesis being that implementing taVNS in the acute period following spontaneous SAH attenuates the expected inflammatory response to hemorrhage and curtails morbidity associated with inflammatory-mediated clinical endpoints.</p><p><b>MATERIALS AND METHODS: </b>The overall objectives for the NAHSaH trial are to 1) Define the impact that taVNS has on SAH-induced inflammatory markers in the plasma and cerebrospinal fluid (CSF), 2) Determine whether taVNS following SAH reduces radiographic vasospasm, and 3) Determine whether taVNS following SAH reduces chronic hydrocephalus. Following presentation to a single enrollment site, enrolled SAH patients are randomly assigned twice daily treatment with either taVNS or sham stimulation for the duration of their intensive care unit stay. Blood and CSF are drawn before initiation of treatment sessions, and then every three days during a patient's hospital stay. Primary endpoints include change in the inflammatory cytokine TNF-α in plasma and cerebrospinal fluid between day 1 and day 13, rate of radiographic vasospasm, and rate of requirement for long-term CSF diversion via a ventricular shunt. Secondary outcomes include exploratory analyses of a panel of additional cytokines, number and type of hospitalized acquired infections, duration of external ventricular drain in days, interventions required for vasospasm, continuous physiology data before, during, and after treatment sessions, hospital length of stay, intensive care unit length of stay, and modified Rankin Scale score (mRS) at admission, discharge, and each at follow-up appointment for up to two years following SAH.</p><p><b>DISCUSSION: </b>Inflammation plays a central role in morbidity following SAH. This NAVSaH trial is innovative because it diverges from the pharmacologic status quo by harnessing a novel non-invasive neuromodulatory approach and its known anti-inflammatory effects to alter the pathophysiology of SAH. The investigation of a new, effective, and rapidly deployable intervention in SAH offers a new route to improve outcomes following SAH.</p><p><b>TRIAL REGISTRATION: </b>Clinical Trials Registered, NCT04557618. Registered on September 21, 2020, and the first patient was enrolled on January 4, 2021.</p>10aAdult10aFemale10aHumans10aInflammation10aMale10aMiddle Aged10aProspective Studies10aRandomized Controlled Trials as Topic10aSubarachnoid Hemorrhage10aTreatment Outcome10aVagus Nerve Stimulation1 aHuguenard, Anna1 aTan, Gansheng1 aJohnson, Gabrielle1 aAdamek, Markus1 aCoxon, Andrew1 aKummer, Terrance1 aOsbun, Joshua1 aVellimana, Ananth1 aLimbrick, David1 aZipfel, Gregory1 aBrunner, Peter1 aLeuthardt, Eric  uhttps://www.neurotechcenter.org/publications/2024/non-invasive-auricular-vagus-nerve-stimulation-subarachnoid-hemorrhage02340nas a2200229   4500008004100000022001400041245012600055210006900181260001600250490000700266520152600273653001001799653002501809653002701834653001101861653004101872100001601913700001901929700001901948700001901967856012401986       2024                        eng d  a2050-084X00aNovel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.0 aNovel cyclic homogeneous oscillation detection method for high a  c2024 Sep 060 v123 a<p>Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/ noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.</p>10aBrain10aElectrocorticography10aElectroencephalography10aHumans10aSignal Processing, Computer-Assisted1 aCho, Hohyun1 aAdamek, Markus1 aWillie, Jon, T1 aBrunner, Peter  uhttps://www.neurotechcenter.org/publications/2024/novel-cyclic-homogeneous-oscillation-detection-method-high-accuracy-002504nas a2200385   4500008004100000022001400041245010500055210006900160260001600229300000900245490000700254520128700261653003001548653001701578653002701595653001101622653002501633653001801658653004601676653001901722100002201741700001901763700001701782700001301799700001901812700001601831700002201847700001801869700002301887700002101910700001901931700002401950700002301974856012101997       2024                        eng d  a2045-232200aA novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study.0 anovel thetacontrolled vibrotactile braincomputer interface to tr  c2024 Feb 10  a34330 v143 a<p>Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4-7 Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study's objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity (1.29 ± 0.25 MAD, p = 0.03, q = 0.05) and pain interference (1.79 ± 1.10 MAD p = 0.03, q = 0.05) scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.</p>10abrain-computer interfaces10aChronic Pain10aElectroencephalography10aHumans10aLongitudinal Studies10aNeurofeedback10aNon-Randomized Controlled Trials as Topic10aPilot Projects1 aDemarest, Phillip1 aRustamov, Nabi1 aSwift, James1 aXie, Tao1 aAdamek, Markus1 aCho, Hohyun1 aWilson, Elizabeth1 aHan, Zhuangyu1 aBelsten, Alexander1 aLuczak, Nicholas1 aBrunner, Peter1 aHaroutounian, Simon1 aLeuthardt, Eric, C  uhttps://www.neurotechcenter.org/publications/2024/novel-theta-controlled-vibrotactile-brain-computer-interface-treat00805nas a2200229   4500008004100000022001400041245013800055210006900193260001300262300000800275490000800283653002600291653001300317653001100330653002100341653001100362653001600373653002500389100002000414700002200434856011900456       2024                        eng d  a1872-895200aOperant up-conditioning of the soleus cutaneous reflex to non-noxious stimuli in a person with chronic incomplete spinal cord injury.0 aOperant upconditioning of the soleus cutaneous reflex to nonnoxi  c2024 Jan  a1-30 v15710aConditioning, Operant10aH-Reflex10aHumans10aMuscle, Skeletal10aReflex10aSpinal Cord10aSpinal Cord Injuries1 aPhipps, Alan, M1 aThompson, Aiko, K  uhttps://www.neurotechcenter.org/publications/2024/operant-conditioning-soleus-cutaneous-reflex-non-noxious-stimuli02755nas a2200337   4500008004100000022001400041245007600055210006900131260001300200300001100213490000800224520171700232653001001949653001501959653001101974653001101985653003101996100001802027700002302045700002602068700002402094700002002118700002102138700002402159700002302183700002402206700002202230700002302252700002302275856011902298       2024                        eng d  a1872-678X00aOptimal approaches to analyzing functional MRI data in glioma patients.0 aOptimal approaches to analyzing functional MRI data in glioma pa  c2024 Feb  a1100110 v4023 a<p><b>BACKGROUND: </b>Resting-state fMRI is increasingly used to study the effects of gliomas on the functional organization of the brain. A variety of preprocessing techniques and functional connectivity analyses are represented in the literature. However, there so far has been no systematic comparison of how alternative methods impact observed results.</p><p><b>NEW METHOD: </b>We first surveyed current literature and identified alternative analytical approaches commonly used in the field. Following, we systematically compared alternative approaches to atlas registration, parcellation scheme, and choice of graph-theoretical measure as regards differentiating glioma patients (N = 59) from age-matched reference subjects (N = 163).</p><p><b>RESULTS: </b>Our results suggest that non-linear, as opposed to affine registration, improves structural match to an atlas, as well as measures of functional connectivity. Functionally- as opposed to anatomically-derived parcellation schemes maximized the contrast between glioma patients and reference subjects. We also demonstrate that graph-theoretic measures strongly depend on parcellation granularity, parcellation scheme, and graph density.</p><p><b>COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: </b>Our current work primarily focuses on technical optimization of rs-fMRI analysis in glioma patients and, therefore, is fundamentally different from the bulk of papers discussing glioma-induced functional network changes. We report that the evaluation of glioma-induced alterations in the functional connectome strongly depends on analytical approaches including atlas registration, choice of parcellation scheme, and graph-theoretical measures.</p>10aBrain10aConnectome10aGlioma10aHumans10aMagnetic Resonance Imaging1 aPark, Ki, Yun1 aShimony, Joshua, S1 aChakrabarty, Satrajit1 aTanenbaum, Aaron, B1 aHacker, Carl, D1 aDonovan, Kara, M1 aLuckett, Patrick, H1 aMilchenko, Mikhail1 aSotiras, Aristeidis1 aMarcus, Daniel, S1 aLeuthardt, Eric, C1 aSnyder, Abraham, Z  uhttps://www.neurotechcenter.org/publications/2024/optimal-approaches-analyzing-functional-mri-data-glioma-patients03236nas a2200481   4500008004100000022001400041245011400055210006900169260001300238300001200251490000800263520183300271653001002104653000902114653001002123653002002133653001102153653001102164653001102175653002102186653003102207653000902238653001602247653002102263653001402284653000902298653002602307100002402333700002402357700001802381700002302399700001902422700003302441700001702474700001602491700001602507700001802523700002102541700002302562700002302585700002302608856012302631       2024                        eng d  a1573-737300aPredicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.0 aPredicting postsurgical functional status in highgrade glioma wi  c2024 Aug  a175-1850 v1693 a<p><b>PURPOSE: </b>High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care.</p><p><b>METHODS: </b>Adult HGG patients (N = 102) from the neurosurgery brain tumor service at Washington University Medical Center were retrospectively recruited. All patients completed structural neuroimaging and resting state functional MRI prior to surgery. Demographics, measures of resting state network connectivity (FC), tumor location, and tumor volume were used to train a random forest classifier to predict functional outcomes based on Karnofsky Performance Status (KPS < 70, KPS ≥ 70).</p><p><b>RESULTS: </b>The models achieved a nested cross-validation accuracy of 94.1% and an AUC of 0.97 in classifying KPS. The strongest predictors identified by the model included FC between somatomotor, visual, auditory, and reward networks. Based on location, the relation of the tumor to dorsal attention, cingulo-opercular, and basal ganglia networks were strong predictors of KPS. Age was also a strong predictor. However, tumor volume was only a moderate predictor.</p><p><b>CONCLUSION: </b>The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.</p>10aAdult10aAged10aBrain10aBrain Neoplasms10aFemale10aGlioma10aHumans10amachine learning10aMagnetic Resonance Imaging10aMale10aMiddle Aged10aNeoplasm Grading10aPrognosis10aRest10aRetrospective Studies1 aLuckett, Patrick, H1 aOlufawo, Michael, O1 aPark, Ki, Yun1 aLamichhane, Bidhan1 aDierker, Donna1 aVerastegui, Gabriel, Trevino1 aLee, John, J1 aYang, Peter1 aKim, Albert1 aButt, Omar, H1 aChheda, Milan, G1 aSnyder, Abraham, Z1 aShimony, Joshua, S1 aLeuthardt, Eric, C  uhttps://www.neurotechcenter.org/publications/2024/predicting-post-surgical-functional-status-high-grade-glioma-resting03132nas a2200433   4500008004100000022001400041245013100055210006900186260001200255300001100267490000800278520179900286653000902085653001002094653001502104653001902119653003002138653002302168653001802191653001102209653002902220100001702249700001902266700002202285700001902307700001702326700002602343700001802369700002702387700002002414700002102434700001802455700002102473700001702494700002402511700001902535700002002554856012402574       2023                        eng d  a1095-957200aComparisons of electrophysiological markers of impaired executive attention after traumatic brain injury and in healthy aging.0 aComparisons of electrophysiological markers of impaired executiv  c07/2023  a1201260 v2743 a<p>Executive attention impairments are a persistent and debilitating consequence of traumatic brain injury (TBI). To make headway towards treating and predicting outcomes following heterogeneous TBI, cognitive impairment specific pathophysiology first needs to be characterized. In a prospective observational study, we measured EEG during the attention network test aimed at detecting alerting, orienting, executive attention and processing speed. The sample (N = 110) of subjects aged 18-86 included those with and without traumatic brain injury: n = 27, complicated mild TBI; n = 5, moderate TBI; n = 10, severe TBI; n = 63, non-brain-injured controls. Subjects with TBI had impairments in processing speed and executive attention. Electrophysiological markers of executive attention processing in the midline frontal regions reveal that, as a group, those with TBI and elderly non-brain-injured controls have reduced responses. We also note that those with TBI and elderly controls have responses that are similar for both low and high-demand trials. In subjects with moderate-severe TBI, reductions in frontal cortical activation and performance profiles are both similar to that of controls who are ∼4 to 7 years older. Our specific observations of frontal response reductions in subjects with TBI and in older adults is consistent with the suggested role of the anterior forebrain mesocircuit as underlying cognitive impairments. Our results provide novel correlative data linking specific pathophysiological mechanisms underlying domain-specific cognitive deficits following TBI and with normal aging. Collectively, our findings provide biomarkers that may serve to track therapeutic interventions and guide development of targeted therapeutics following brain injuries.</p>10aAged10aAging10aBiomarkers10aBrain Injuries10aBrain Injuries, Traumatic10aExecutive Function10aHealthy Aging10aHumans10aNeuropsychological Tests1 aKim, Nayoung1 aJamison, Keith1 aJaywant, Abhishek1 aGaretti, Jacob1 aBlunt, Emily1 aRoyChoudhury, Arindam1 aButler, Tracy1 aDams-O'Connor, Kristen1 aKhedr, Shahenda1 aChen, Chun-Cheng1 aShetty, Teena1 aWinchell, Robert1 aHill, Jeremy1 aSchiff, Nicholas, D1 aKuceyeski, Amy1 aShah, Sudhin, A  uhttps://www.neurotechcenter.org/publications/2023/comparisons-electrophysiological-markers-impaired-executive-attention02285nas a2200253   4500008004100000022001400041245007600055210006900131260001200200300001400212490000800226520146600234653001001700653002901710653001301739653001101752653002101763653002201784653004401806100002001850700002401870700002201894856011501916       2023                        eng d  a1432-110600aEffects of active and sham tDCS on the soleus H-reflex during standing.0 aEffects of active and sham tDCS on the soleus Hreflex during sta  c06/2023  a1611-16220 v2413 a<p>Weak transcranial direct current stimulation (tDCS) is known to affect corticospinal excitability and enhance motor skill acquisition, whereas its effects on spinal reflexes in actively contracting muscles are yet to be established. Thus, in this study, we examined the acute effects of Active and Sham tDCS on the soleus H-reflex during standing. In fourteen adults without known neurological conditions, the soleus H-reflex was repeatedly elicited at just above M-wave threshold throughout 30 min of Active (N = 7) or Sham (N = 7) 2-mA tDCS over the primary motor cortex in standing. The maximum H-reflex (H) and M-wave (M) were also measured before and immediately after 30 min of tDCS. The soleus H-reflex amplitudes became significantly larger (by 6%) ≈1 min into Active or Sham tDCS and gradually returned toward the pre-tDCS values, on average, within 15 min. With Active tDCS, the amplitude reduction from the initial increase appeared to occur more swiftly than with Sham tDCS. An acute temporary increase in the soleus H-reflex amplitude within the first minute of Active and Sham tDCS found in this study indicates a previously unreported effect of tDCS on the H-reflex excitability. The present study suggests that neurophysiological characterization of Sham tDCS effects is just as important as investigating Active tDCS effects in understanding and defining acute effects of tDCS on the excitability of spinal reflex pathways.</p>10aAdult10aEvoked Potentials, Motor10aH-Reflex10aHumans10aMuscle, Skeletal10aStanding Position10aTranscranial Direct Current Stimulation1 aMcCane, Lynn, M1 aWolpaw, Jonathan, R1 aThompson, Aiko, K  uhttps://www.neurotechcenter.org/publications/2023/effects-active-and-sham-tdcs-soleus-h-reflex-during-standing03180nas a2200277   4500008004100000022001400041245010400055210006900159260001200228300001100240490000800251520226600259653002102525653002902546653001102575653001702586653001302603653002102616653001202637653002102649653003802670100002502708700002202733700002402755856012302779       2023                        eng d  a1522-159800aThe human motor cortex contributes to gravity compensation to maintain posture and during reaching.0 ahuman motor cortex contributes to gravity compensation to mainta  c01/2023  a83-1010 v1293 a<p>The neural control of posture and movement is interdependent. During voluntary movement, the neural motor command is executed by the motor cortex through the corticospinal tract and its collaterals and subcortical targets. Here we address the question of whether the control mechanism for the postural adjustments at nonmoving joints is also involved in overcoming gravity at the moving joints. We used single-pulse transcranial magnetic stimulation to measure the corticospinal excitability in humans during postural and reaching tasks. We hypothesized that the corticospinal excitability is proportional to background muscle activity and the gravity-related joint moments during both static postures and reaching movements. To test this hypothesis, we used visual targets in virtual reality to instruct five postures and three movements with or against gravity. We then measured the amplitude and gain of motor evoked potentials in multiple arm and hand muscles at several phases of the reaching motion and during static postures. The stimulation caused motor evoked potentials in all muscles that were proportional to the muscle activity. During both static postures and reaching movements, the muscle activity and the corticospinal contribution to these muscles changed in proportion with the postural moments needed to support the arm against gravity, supporting the hypothesis. Notably, these changes happened not only in antigravity muscles. Altogether, these results provide evidence that the changes in corticospinal excitability cause muscle cocontraction that modulates limb stiffness. This suggests that the motor cortex is involved in producing postural adjustments that support the arm against gravity during posture maintenance and reaching. Animal studies suggest that the corticospinal tract and its collaterals are crucial for producing postural adjustments that accompany movement in limbs other than the moving limb. Here we provide evidence for a similar control schema for both arm posture maintenance and gravity compensation during movement of the same limb. The observed interplay between the postural and movement control signals within the corticospinal tract may help explain the underlying neural motor deficits after stroke.</p>10aElectromyography10aEvoked Potentials, Motor10aHumans10aMotor Cortex10aMovement10aMuscle, Skeletal10aPosture10aPyramidal Tracts10aTranscranial Magnetic Stimulation1 aHardesty, Russell, L1 aEllaway, Peter, H1 aGritsenko, Valeriya  uhttps://www.neurotechcenter.org/publications/2023/human-motor-cortex-contributes-gravity-compensation-maintain-posture03030nas a2200265   4500008004100000022001400041245016500055210006900220260001600289490000700305520208500312653002502397653002102422653001302443653001102456653002102467653002202488653002602510100002502536700001702561700002202578700002202600700002202622856012002644       2023                        eng d  a1741-255200aMethods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications.0 aMethods for automated delineation and assessment of EMG response  c2023 Jul 210 v203 a<p>Surface electromyography measurements of the Hoffmann (H-) reflex are essential in a wide range of neuroscientific and clinical applications. One promising emerging therapeutic application is H-reflex operant conditioning, whereby a person is trained to modulate the H-reflex, with generalized beneficial effects on sensorimotor function in chronic neuromuscular disorders. Both traditional diagnostic and novel realtime therapeutic applications rely on accurate definitions of the H-reflex and M-wave temporal bounds, which currently depend on expert case-by-case judgment. The current study automates such judgments.Our novel wavelet-based algorithm automatically determines temporal extent and amplitude of the human soleus H-reflex and M-wave. In each of 20 participants, the algorithm was trained on data from a preliminary 3 or 4 min recruitment-curve measurement. Output was evaluated on parametric fits to subsequent sessions' recruitment curves (92 curves across all participants) and on the conditioning protocol's subsequent baseline trials (∼1200 per participant) performed near. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later.Automatic bounds agreed well with manual estimates: 95% lay within ±2.5 ms. The resulting H-reflex magnitude estimates showed excellent agreement (97.5% average across participants) between automatic and retrospective bounds regarding which trials would be considered successful for operant conditioning. Recruitment-curve parameters also agreed well between automatic and manual methods: 95% of the automatic estimates of the current required to elicitfell within±1.4%of the retrospective estimate; for the 'threshold' current that produced an M-wave 10% of maximum, this value was±3.5%.Such dependable automation of M-wave and H-reflex definition should make both established and emerging H-reflex protocols considerably less vulnerable to inter-personnel variability and human error, increasing translational potential.</p>10aElectric Stimulation10aElectromyography10aH-Reflex10aHumans10aMuscle, Skeletal10aPeripheral Nerves10aRetrospective Studies1 aMcKinnon, Michael, L1 aHill, Jeremy1 aCarp, Jonathan, S1 aDellenbach, Blair1 aThompson, Aiko, K  uhttps://www.neurotechcenter.org/publications/2023/methods-automated-delineation-and-assessment-emg-responses-evoked02553nas a2200325   4500008004100000022001400041245011400055210006900169260001200238300001000250490000800260520151900268653002401787653001001811653001801821653002001839653002501859653001101884653001301895100002401908700002001932700002401952700002301976700002201999700002102021700002502042700001902067700001902086856012202105       2023                        eng d  a1872-895200aPassive functional mapping of receptive language cortex during general anesthesia using electrocorticography.0 aPassive functional mapping of receptive language cortex during g  c03/2023  a31-440 v1473 a<p><b>OBJECTIVE: </b>To investigate the feasibility of passive functional mapping in the receptive language cortex during general anesthesia using electrocorticographic (ECoG) signals.</p><p><b>METHODS: </b>We used subdurally placed ECoG grids to record cortical responses to speech stimuli during awake and anesthesia conditions. We identified the cortical areas with significant responses to the stimuli using the spectro-temporal consistency of the brain signal in the broadband gamma (BBG) frequency band (70-170 Hz).</p><p><b>RESULTS: </b>We found that ECoG BBG responses during general anesthesia effectively identify cortical regions associated with receptive language function. Our analyses demonstrated that the ability to identify receptive language cortex varies across different states and depths of anesthesia. We confirmed these results by comparing them to receptive language areas identified during the awake condition. Quantification of these results demonstrated an average sensitivity and specificity of passive language mapping during general anesthesia to be 49±7.7% and 100%, respectively.</p><p><b>CONCLUSION: </b>Our results demonstrate that mapping receptive language cortex in patients during general anesthesia is feasible.</p><p><b>SIGNIFICANCE: </b>Our proposed protocol could greatly expand the population of patients that can benefit from passive language mapping techniques, and could eliminate the risks associated with electrocortical stimulation during an awake craniotomy.</p>10aAnesthesia, General10aBrain10aBrain Mapping10aCerebral Cortex10aElectrocorticography10aHumans10aLanguage1 aNourmohammadi, Amin1 aSwift, James, R1 ade Pesters, Adriana1 aGuay, Christian, S1 aAdamo, Matthew, A1 aDalfino, John, C1 aRitaccio, Anthony, L1 aSchalk, Gerwin1 aBrunner, Peter  uhttps://www.neurotechcenter.org/publications/2023/passive-functional-mapping-receptive-language-cortex-during-general04323nas a2200877   4500008004100000022001400041245008800055210006900143260001200212300001200224490000800236520184100244653001202085653001802097653001002115653001402125653002202139653000902161653000902170653001102179653001102190653002002201653001102221653003102232653001702263653001002280100002002290700002502310700001902335700002202354700002002376700002302396700002202419700002202441700002302463700002602486700001602512700002202528700002202550700002102572700001702593700002002610700002002630700002202650700002202672700001902694700003002713700002302743700002202766700002502788700001302813700002102826700002702847700002302874700002002897700002302917700002002940700001902960700001902979700002302998700002103021700001703042700002103059700002203080700002303102700002303125700001803148700002203166700002103188700002403209700002303233700002403256700002003280700002503300856012003325       2023                        eng d  a1476-468700aA somato-cognitive action network alternates with effector regions in motor cortex.0 asomatocognitive action network alternates with effector regions   c05/2023  a351-3590 v6173 a<p>Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations, despite evidence for concentric functional zones and maps of complex actions. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action and physiological control, arousal, errors and pain. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions and connectivity to internal organs such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate-isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.</p>10aAnimals10aBrain Mapping10aChild10aCognition10aDatasets as Topic10aFoot10aHand10aHumans10aInfant10aInfant, Newborn10aMacaca10aMagnetic Resonance Imaging10aMotor Cortex10aMouth1 aGordon, Evan, M1 aChauvin, Roselyne, J1 aVan, Andrew, N1 aRajesh, Aishwarya1 aNielsen, Ashley1 aNewbold, Dillan, J1 aLynch, Charles, J1 aSeider, Nicole, A1 aKrimmel, Samuel, R1 aScheidter, Kristen, M1 aMonk, Julia1 aMiller, Ryland, L1 aMetoki, Athanasia1 aMontez, David, F1 aZheng, Annie1 aElbau, Immanuel1 aMadison, Thomas1 aNishino, Tomoyuki1 aMyers, Michael, J1 aKaplan, Sydney1 aD'Andrea, Carolina, Badke1 aDemeter, Damion, V1 aFeigelis, Matthew1 aRamirez, Julian, S B1 aXu, Ting1 aBarch, Deanna, M1 aSmyser, Christopher, D1 aRogers, Cynthia, E1 aZimmermann, Jan1 aBotteron, Kelly, N1 aPruett, John, R1 aWillie, Jon, T1 aBrunner, Peter1 aShimony, Joshua, S1 aKay, Benjamin, P1 aMarek, Scott1 aNorris, Scott, A1 aGratton, Caterina1 aSylvester, Chad, M1 aPower, Jonathan, D1 aListon, Conor1 aGreene, Deanna, J1 aRoland, Jarod, L1 aPetersen, Steven, E1 aRaichle, Marcus, E1 aLaumann, Timothy, O1 aFair, Damien, A1 aDosenbach, Nico, U F  uhttps://www.neurotechcenter.org/publications/2023/somato-cognitive-action-network-alternates-effector-regions-motor02579nas a2200289   4500008004100000022001400041245014200055210006900197260001200266300001400278490000800292520157400300653003401874653003001908653002701938653001101965653002201976653001101998100002802009700001602037700002502053700002102078700002102099700002402120700002402144856012102168       2023                        eng d  a1432-145900aTowards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations.0 aTowards clinical application of implantable braincomputer interf  c03/2023  a1323-13360 v2703 a<p>Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain-computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease, among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs.</p>10aAmyotrophic Lateral Sclerosis10abrain-computer interfaces10aElectroencephalography10aHumans10aSelf-Help Devices10aSpeech1 aVansteensel, Mariska, J1 aKlein, Eran1 avan Thiel, Ghislaine1 aGaytant, Michael1 aSimmons, Zachary1 aWolpaw, Jonathan, R1 aVaughan, Theresa, M  uhttps://www.neurotechcenter.org/publications/2023/towards-clinical-application-implantable-brain-computer-interfaces03130nas a2200313   4500008004100000022001400041245013900055210006900194260001500263520206900278653002002347653002602367653002102393653002202414653001302436653001102449653002702460653002502487100001702512700001702529700001902546700002402565700002302589700002102612700001402633700002402647700002202671856012302693       2022                        eng d  a1940-087X00aThe Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders.0 aEvoked Potential Operant Conditioning System EPOCS A Research To  c2022 08 253 a<p>The Evoked Potential Operant Conditioning System (EPOCS) is a software tool that implements protocols for operantly conditioning stimulus-triggered muscle responses in people with neuromuscular disorders, which in turn can improve sensorimotor function when applied appropriately. EPOCS monitors the state of specific target muscles-e.g., from surface electromyography (EMG) while standing, or from gait cycle measurements while walking on a treadmill-and automatically triggers calibrated stimulation when pre-defined conditions are met. It provides two forms of feedback that enable a person to learn to modulate the targeted pathway's excitability. First, it continuously monitors ongoing EMG activity in the target muscle, guiding the person to produce a consistent level of activity suitable for conditioning. Second, it provides immediate feedback of the response size following each stimulation and indicates whether it has reached the target value. To illustrate its use, this article describes a protocol through which a person can learn to decrease the size of the Hoffmann reflex-the electrically-elicited analog of the spinal stretch reflex-in the soleus muscle. Down-conditioning this pathway's excitability can improve walking in people with spastic gait due to incomplete spinal cord injury. The article demonstrates how to set up the equipment; how to place stimulating and recording electrodes; and how to use the free software to optimize electrode placement, measure the recruitment curve of direct motor and reflex responses, measure the response without operant conditioning, condition the reflex, and analyze the resulting data. It illustrates how the reflex changes over multiple sessions and how walking improves. It also discusses how the system can be applied to other kinds of evoked responses and to other kinds of stimulation, e.g., motor evoked potentials to transcranial magnetic stimulation; how it can address various clinical problems; and how it can support research studies of sensorimotor function in health and disease.</p>10aChronic Disease10aConditioning, Operant10aElectromyography10aEvoked Potentials10aH-Reflex10aHumans10aNeuromuscular Diseases10aSpinal Cord Injuries1 aHill, Jeremy1 aGupta, Disha1 aEftekhar, Amir1 aBrangaccio, Jodi, A1 aNorton, James, J S1 aMcLeod, Michelle1 aFake, Tim1 aWolpaw, Jonathan, R1 aThompson, Aiko, K  uhttps://www.neurotechcenter.org/publications/2022/evoked-potential-operant-conditioning-system-epocs-research-tool-and02437nas a2200241   4500008004100000022001400041245007500055210006900130260001200199300001400211490000800225520167700233653003001910653001201940653002701952653001101979653002401990653001302014653001302027100002402040700001802064856011302082       2022                        eng d  a1469-779300aHeksor: the central nervous system substrate of an adaptive behaviour.0 aHeksor the central nervous system substrate of an adaptive behav  c08/2022  a3423-34520 v6003 a<p>Over the past half-century, the largely hardwired central nervous system (CNS) of 1970 has become the ubiquitously plastic CNS of today, in which change is the rule not the exception. This transformation complicates a central question in neuroscience: how are adaptive behaviours - behaviours that serve the needs of the individual - acquired and maintained through life? It poses a more basic question: how do many adaptive behaviours share the ubiquitously plastic CNS? This question compels neuroscience to adopt a new paradigm. The core of this paradigm is a CNS entity with unique properties, here given the name heksor from the Greek hexis. A heksor is a distributed network of neurons and synapses that changes itself as needed to maintain the key features of an adaptive behaviour, the features that make the behaviour satisfactory. Through their concurrent changes, the numerous heksors that share the CNS negotiate the properties of the neurons and synapses that they all use. Heksors keep the CNS in a state of negotiated equilibrium that enables each heksor to maintain the key features of its behaviour. The new paradigm based on heksors and the negotiated equilibrium they create is supported by animal and human studies of interactions among new and old adaptive behaviours, explains otherwise inexplicable results, and underlies promising new approaches to restoring behaviours impaired by injury or disease. Furthermore, the paradigm offers new and potentially important answers to extant questions, such as the generation and function of spontaneous neuronal activity, the aetiology of muscle synergies, and the control of homeostatic plasticity.</p>10aAdaptation, Psychological10aAnimals10acentral nervous system10aHumans10aNeuronal Plasticity10aPlastics10aSynapses1 aWolpaw, Jonathan, R1 aKamesar, Adam  uhttps://www.neurotechcenter.org/publications/2022/heksor-central-nervous-system-substrate-adaptive-behaviour02367nas a2200337   4500008004100000022001400041245007800055210006900133260001200202300000900214490000700223520135600230653001001586653002701596653000901623653001101632653002701643653001601670653001201686100002101698700002101719700002201740700002401762700001801786700002601804700002101830700001901851700002001870700001901890856012001909       2022                        eng d  a2045-232200aNeural oscillations during motor imagery of complex gait: an HdEEG study.0 aNeural oscillations during motor imagery of complex gait an HdEE  c03/2022  a43140 v123 a<p>The aim of this study was to investigate differences between usual and complex gait motor imagery (MI) task in healthy subjects using high-density electroencephalography (hdEEG) with a MI protocol. We characterized the spatial distribution of α- and β-bands oscillations extracted from hdEEG signals recorded during MI of usual walking (UW) and walking by avoiding an obstacle (Dual-Task, DT). We applied a source localization algorithm to brain regions selected from a large cortical-subcortical network, and then we analyzed α and β bands Event-Related Desynchronizations (ERDs). Nineteen healthy subjects visually imagined walking on a path with (DT) and without (UW) obstacles. Results showed in both gait MI tasks, α- and β-band ERDs in a large cortical-subcortical network encompassing mostly frontal and parietal regions. In most of the regions, we found α- and β-band ERDs in the DT compared with the UW condition. Finally, in the β band, significant correlations emerged between ERDs and scores in imagery ability tests. Overall we detected MI gait-related α- and β-band oscillations in cortical and subcortical areas and significant differences between UW and DT MI conditions. A better understanding of gait neural correlates may lead to a better knowledge of pathophysiology of gait disturbances in neurological diseases.</p>10aBrain10aElectroencephalography10aGait10aHumans10aImagery, Psychotherapy10aImagination10aWalking1 aPutzolu, Martina1 aSamogin, Jessica1 aCosentino, Carola1 aMezzarobba, Susanna1 aBonassi, Gaia1 aLagravinese, Giovanna1 aVato, Alessandro1 aMantini, Dante1 aAvanzino, Laura1 aPelosin, Elisa  uhttps://www.neurotechcenter.org/publications/2022/neural-oscillations-during-motor-imagery-complex-gait-hdeeg-study02454nas a2200265   4500008004100000022001400041245007300055210006900128260001200197300001400209490000800223520163500231653001001866653001601876653002101892653001301913653001101926653002001937653002101957100001801978700002401996700002602020700002202046856012002068       2022                        eng d  a1432-110600aSoleus H-reflex modulation during a double-legged drop landing task.0 aSoleus Hreflex modulation during a doublelegged drop landing tas  c04/2022  a1093-11030 v2403 a<p>Muscle spindle afferent feedback is modulated during different phases of locomotor tasks in a way that facilitates task goals. However, only a few studies have studied H-reflex modulation during landing. This study aimed to characterize soleus (SOL) H-reflex modulation during the flight and early landing period of drop landings. Since landing presumably involves a massive increase in spindle afferent firing due to rapid SOL muscle stretching, we hypothesized H-reflex size would decrease near landing reflecting neural modulation to prevent excessive motoneuron excitation. The soleus H-reflex was recorded during drop landings from a 30 cm height in nine healthy adults. Electromyography (SOL, tibialis anterior (TA), medial gastrocnemius, and vastus lateralis), ankle and knee joint motion and ground reaction force were recorded during landings. Tibial nerve stimulation was timed to elicit H-reflexes during the flight and early ground contact period (five 30 ms Bins from 90 ms before to 60 ms after landing). The H-reflexes recorded after landing (0-30 and 30-60 ms) were significantly smaller (21-36% less) than that recorded during the flight periods (90-0 ms before ground contact; P ≤ 0.004). The decrease in H-reflex size not occurring until after ground contact indicates a time-critical modulation of reflex gain during the last 30 ms of flight (i.e., time of tibial nerve stimulation). H-reflex size reduction after ground contact supports a probable neural strategy to prevent excessive reflex-mediated muscle activation and thereby facilitates appropriate musculotendon and joint stiffness.</p>10aAdult10aAnkle Joint10aElectromyography10aH-Reflex10aHumans10aMuscle Spindles10aMuscle, Skeletal1 aLyle, Mark, A1 aMcLeod, Michelle, M1 aPouliot, Bridgette, A1 aThompson, Aiko, K  uhttps://www.neurotechcenter.org/publications/2022/soleus-h-reflex-modulation-during-double-legged-drop-landing-task01696nas a2200289   4500008004100000022001400041245006300055210006000118260001600178490000700194520079100201653003000992653001701022653002701039653003001066653001101096653001001107653002301117653002001140100002301160700002201183700002201205700002201227700002401249700002401273856010901297       2021                        eng d  a1741-255200aBrain-computer interface-based assessment of color vision.0 aBraincomputer interfacebased assessment of color vision  c2021 Nov 260 v183 a<p>Present methods for assessing color vision require the person's active participation. Here we describe a brain-computer interface-based method for assessing color vision that does not require the person's participation.This method uses steady-state visual evoked potentials to identify metamers-two light sources that have different spectral distributions but appear to the person to be the same color.We demonstrate that: minimization of the visual evoked potential elicited by two flickering light sources identifies the metamer; this approach can distinguish people with color-vision deficits from those with normal color vision; and this metamer-identification process can be automated.This new method has numerous potential clinical, scientific, and industrial applications.</p>10abrain-computer interfaces10aColor Vision10aElectroencephalography10aEvoked Potentials, Visual10aHumans10aLight10aPhotic Stimulation10aResearch Design1 aNorton, James, J S1 aDiRisio, Grace, F1 aCarp, Jonathan, S1 aNorton, Amanda, E1 aKochan, Nicholas, S1 aWolpaw, Jonathan, R  uhttps://www.neurotechcenter.org/publications/2021/brain-computer-interface-based-assessment-color-vision02272nas a2200337   4500008004100000022001400041245011100055210006900166260001200235300001100247490000800258520124200266653002501508653001001533653000901543653001701552653002001569653001801589653002601607653002501633653001101658653001101669653000901680653001601689100002301705700003201728700001901760700001901779700001901798856011701817       2021                        eng d  a1095-957200aModulation in cortical excitability disrupts information transfer in perceptual-level stimulus processing.0 aModulation in cortical excitability disrupts information transfe  c11/2021  a1184980 v2433 a<p>Despite significant interest in the neural underpinnings of behavioral variability, little light has been shed on the cortical mechanism underlying the failure to respond to perceptual-level stimuli. We hypothesized that cortical activity resulting from perceptual-level stimuli is sensitive to the moment-to-moment fluctuations in cortical excitability, and thus may not suffice to produce a behavioral response. We tested this hypothesis using electrocorticographic recordings to follow the propagation of cortical activity in six human subjects that responded to perceptual-level auditory stimuli. Here we show that for presentations that did not result in a behavioral response, the likelihood of cortical activity decreased from auditory cortex to motor cortex, and was related to reduced local cortical excitability. Cortical excitability was quantified using instantaneous voltage during a short window prior to cortical activity onset. Therefore, when humans are presented with an auditory stimulus close to perceptual-level threshold, moment-by-moment fluctuations in cortical excitability determine whether cortical responses to sensory stimulation successfully connect auditory input to a resultant behavioral response.</p>10aAcoustic Stimulation10aAdult10aAged10aAlpha Rhythm10aAuditory Cortex10aBrain Mapping10aCortical Excitability10aElectrocorticography10aFemale10aHumans10aMale10aMiddle Aged1 aMoheimanian, Ladan1 aParaskevopoulou, Sivylla, E1 aAdamek, Markus1 aSchalk, Gerwin1 aBrunner, Peter  uhttps://www.neurotechcenter.org/publications/2021/modulation-cortical-excitability-disrupts-information-transfer02303nas a2200265   4500008004100000022001400041245007700055210006900132260000900201300001400210490000700224520143600231653001501667653003001682653002701712653003001739653001101769653002301780100002101803700002301824700002401847700001801871700002801889856012001917       2021                        eng d  a1558-021000aA Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.0 aVotingEnhanced DynamicWindowLength Classifier for SSVEPBased BCI  c2021  a1766-17730 v293 a<p>We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.</p>10aAlgorithms10abrain-computer interfaces10aElectroencephalography10aEvoked Potentials, Visual10aHumans10aPhotic Stimulation1 aHabibzadeh, Hadi1 aNorton, James, J S1 aVaughan, Theresa, M1 aSoyata, Tolga1 aZois, Daphney-Stavroula  uhttps://www.neurotechcenter.org/publications/2021/voting-enhanced-dynamic-window-length-classifier-ssvep-based-bcis02753nas a2200373   4500008004100000022001400041245011800055210006900173260001200242300001100254490000800265520163200273653001001905653001501915653001701930653002001947653001501967653002501982653001102007653001702018653001102035653000902046653001602055653001402071653002802085653001802113653001602131100003202147700002102179700001902200700001902219700001902238856012202257       2021                        eng d  a1095-957200aWithin-subject reaction time variability: Role of cortical networks and underlying neurophysiological mechanisms.0 aWithinsubject reaction time variability Role of cortical network  c08/2021  a1181270 v2373 a<p>Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject's task is the same from trial to trial.</p>10aAdult10aAlgorithms10aAlpha Rhythm10aCerebral Cortex10aConnectome10aElectrocorticography10aFemale10aGamma Rhythm10aHumans10aMale10aMiddle Aged10aNerve Net10aPsychomotor Performance10aReaction Time10aYoung Adult1 aParaskevopoulou, Sivylla, E1 aCoon, William, G1 aBrunner, Peter1 aMiller, Kai, J1 aSchalk, Gerwin  uhttps://www.neurotechcenter.org/publications/2021/within-subject-reaction-time-variability-role-cortical-networks-and02570nas a2200325   4500008004100000022001400041245013800055210006900193260001200262300001100274490000800285520150800293653002601801653002601827653001601853653001301869653001101882653003101893100002001924700002301944700002001967700002001987700001802007700002302025700001902048700001902067700002402086700001902110856011502129       2020                        eng d  a1873-423500aBreathable, large-area epidermal electronic systems for recording electromyographic activity during operant conditioning of H-reflex.0 aBreathable largearea epidermal electronic systems for recording   c10/2020  a1124040 v1653 a<p>Operant conditioning of Hoffmann's reflex (H-reflex) is a non-invasive and targeted therapeutic intervention for patients with movement disorders following spinal cord injury. The reflex-conditioning protocol uses electromyography (EMG) to measure reflexes from specific muscles elicited using transcutaneous electrical stimulation. Despite recent advances in wearable electronics, existing EMG systems that measure muscle activity for operant conditioning of spinal reflexes still use rigid metal electrodes with conductive gels and aggressive adhesives, while requiring precise positioning to ensure reliability of data across experimental sessions. Here, we present the first large-area epidermal electronic system (L-EES) and demonstrate its use in every step of the reflex-conditioning protocol. The L-EES is a stretchable and breathable composite of nanomembrane electrodes (16 electrodes in a four by four array), elastomer, and fabric. The nanomembrane electrode array enables EMG recording from a large surface area on the skin and the breathable elastomer with fabric is biocompatible and comfortable for patients. We show that L-EES can record direct muscle responses (M-waves) and H-reflexes, both of which are comparable to those recorded using conventional EMG recording systems. In addition, L-EES may improve the reflex-conditioning protocol; it has potential to automatically optimize EMG electrode positioning, which may reduce setup time and error across experimental sessions.</p>10aBiosensing Techniques10aConditioning, Operant10aElectronics10aH-Reflex10aHumans10aReproducibility of Results1 aKwon, Young-Tae1 aNorton, James, J S1 aCutrone, Andrew1 aLim, Hyo-Ryoung1 aKwon, Shinjae1 aChoi, Jeongmoon, J1 aKim, Hee, Seok1 aJang, Young, C1 aWolpaw, Jonathan, R1 aYeo, Woon-Hong  uhttps://www.neurotechcenter.org/publications/2020/breathable-large-area-epidermal-electronic-systems-recording02869nas a2200301   4500008004100000022001400041245009000055210006900145260001200214300001400226490000700240520184800247653003402095653003002129653001802159653002702177653001102204653003202215100002902247700001902276700002602295700002502321700003202346700002502378700002102403700002102424856012202445       2020                        eng d  a1558-021000aEnhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System.0 aEnhancing Communication for People in LateStage ALS Using an fNI  c05/2020  a1198-12070 v283 a<p><b>OBJECTIVE: </b>Brain-computer interface (BCI) based communication remains a challenge for people with later-stage amyotrophic lateral sclerosis (ALS) who lose all voluntary muscle control. Although recent studies have demonstrated the feasibility of functional near-infrared spectroscopy (fNIRS) to successfully control BCIs primarily for healthy cohorts, these systems are yet inefficient for people with severe motor disabilities like ALS. In this study, we developed a new fNIRS-based BCI system in concert with a single-trial Visuo-Mental (VM) paradigm to investigate the feasibility of enhanced communication for ALS patients, particularly those in the later stages of the disease.</p><p><b>METHODS: </b>In the first part of the study, we recorded data from six ALS patients using our proposed protocol (fNIRS-VM) and compared the results with the conventional electroencephalography (EEG)-based multi-trial P3Speller (P3S). In the second part, we recorded longitudinal data from one patient in the late locked-in state (LIS) who had fully lost eye-gaze control. Using statistical parametric mapping (SPM) and correlation analysis, the optimal channels and hemodynamic features were selected and used in linear discriminant analysis (LDA).</p><p><b>RESULTS: </b>Over all the subjects, we obtained an average accuracy of 81.3%±5.7% within comparatively short times (< 4 sec) in the fNIRS-VM protocol relative to an average accuracy of 74.0%±8.9% in the P3S, though not competitive in patients with no substantial visual problems. Our longitudinal analysis showed substantially superior accuracy using the proposed fNIRS-VM protocol (73.2%±2.0%) over the P3S (61.8%±1.5%).</p><p><b>SIGNIFICANCE: </b>Our findings indicate the potential efficacy of our proposed system for communication and control for late-stage ALS patients.</p>10aAmyotrophic Lateral Sclerosis10abrain-computer interfaces10aCommunication10aElectroencephalography10aHumans10aSpectroscopy, Near-Infrared1 aBorgheai, Seyyed, Bahram1 aMcLinden, John1 aZisk, Alyssa, Hillary1 aHosni, Sarah, Ismail1 aDeligani, Roohollah, Jafari1 aAbtahi, Mohammadreza1 aMankodiya, Kunal1 aShahriari, Yalda  uhttps://www.neurotechcenter.org/publications/2020/enhancing-communication-people-late-stage-als-using-fnirs-based-bci03177nas a2200493   4500008004100000022001400041245012000055210006900175260001200244300001200256490000800268520172300276653001801999653002002017653001502037653001102052653002202063653001102085653001102096653001402107653001302121653000902134653001602143653003102159653001402190653002602204653001302230653001802243653001602261100001802277700002302295700002102318700002302339700002602362700002402388700001802412700001902430700001402449700002202463700002202485700002602507700003102533856011902564       2020                        eng d  a1573-737300aPotential differences between monolingual and bilingual patients in approach and outcome after awake brain surgery.0 aPotential differences between monolingual and bilingual patients  c07/2020  a587-5980 v1483 a<p><b>INTRODUCTION: </b>20.8% of the United States population and 67% of the European population speak two or more languages. Intraoperative different languages, mapping, and localization are crucial. This investigation aims to address three questions between BL and ML patients: (1) Are there differences in complications (i.e. seizures) and DECS techniques during intra-operative brain mapping? (2) Is EOR different? and (3) Are there differences in the recovery pattern post-surgery?</p><p><b>METHODS: </b>Data from 56 patients that underwent left-sided awake craniotomy for tumors infiltrating possible dominant hemisphere language areas from September 2016 to June 2019 were identified and analyzed in this study; 14 BL and 42 ML control patients. Patient demographics, education level, and the age of language acquisition were documented and evaluated. fMRI was performed on all participants.</p><p><b>RESULTS: </b>0 (0%) BL and 3 (7%) ML experienced intraoperative seizures (P = 0.73). BL patients received a higher direct DECS current in comparison to the ML patients (average = 4.7, 3.8, respectively, P = 0.03). The extent of resection was higher in ML patients in comparison to the BL patients (80.9 vs. 64.8, respectively, P = 0.04). The post-operative KPS scores were higher in BL patients in comparison to ML patients (84.3, 77.4, respectively, P = 0.03). BL showed lower drop in post-operative KPS in comparison to ML patients (- 4.3, - 8.7, respectively, P = 0.03).</p><p><b>CONCLUSION: </b>We show that BL patients have a lower incidence of intra-operative seizures, lower EOR, higher post-operative KPS and tolerate higher DECS current, in comparison to ML patients.</p>10aBrain Mapping10aBrain Neoplasms10aCraniotomy10aFemale10aFollow-Up Studies10aGlioma10aHumans10aIncidence10aLanguage10aMale10aMiddle Aged10aMonitoring, Intraoperative10aPrognosis10aRetrospective Studies10aSeizures10aUnited States10aWakefulness1 aReFaey, Karim1 aTripathi, Shashwat1 aBhargav, Adip, G1 aGrewal, Sanjeet, S1 aMiddlebrooks, Erik, H1 aSabsevitz, David, S1 aJentoft, Mark1 aBrunner, Peter1 aWu, Adela1 aTatum, William, O1 aRitaccio, Anthony1 aChaichana, Kaisorn, L1 aQuinones-Hinojosa, Alfredo  uhttps://www.neurotechcenter.org/publications/2020/potential-differences-between-monolingual-and-bilingual-patients02982nas a2200313   4500008004100000022001400041245013700055210006900192260001200261300001100273490000700284520198000291653001002271653001802281653002502299653002602324653002702350653001102377653003102388100001602419700001702435700001502452700001902467700001402486700001902500700001602519700001902535856011402554       2019                        eng d  a1741-255200aiEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes.0 aiEEGview an opensource multifunction GUIbased Matlab toolbox for  c12/2019  a0160160 v173 a<p><b>OBJECTIVE: </b>The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand for more integrated, easy-operation and versatile tools for electrode localization and visualization. With the aim of addressing this need, we develop an easy-to-use and multifunction toolbox called iEEGview, which can be used for the localization and visualization of human intracranial electrodes.</p><p><b>APPROACH: </b>iEEGview is written in Matlab scripts and implemented with a GUI. From the GUI, by taking only pre-implant MRI and post-implant CT images as input, users can directly run the full localization pipeline including brain segmentation, image co-registration, electrode reconstruction, anatomical information identification, activation map generation and electrode projection from native brain space into common brain space for group analysis. Additionally, iEEGview implements methods for brain shift correction, visual location inspection on MRI slices and computation of certainty index in anatomical label assignment.</p><p><b>MAIN RESULTS: </b>All the introduced functions of iEEGview work reliably and successfully, and are tested by images from 28 human subjects implanted with depth and/or subdural electrodes.</p><p><b>SIGNIFICANCE: </b>iEEGview is the first public Matlab GUI-based software for intracranial electrode localization and visualization that holds integrated capabilities together within one pipeline. iEEGview promotes convenience and efficiency for the localization process, provides rich localization information for further analysis and offers solutions for addressing raised technical challenges. Therefore, it can serve as a useful tool in facilitating iEEG studies.</p>10aBrain10aBrain Mapping10aElectrocorticography10aElectrodes, Implanted10aElectroencephalography10aHumans10aMagnetic Resonance Imaging1 aLi, Guangye1 aJiang, Shize1 aChen, Chen1 aBrunner, Peter1 aWu, Zehan1 aSchalk, Gerwin1 aChen, Liang1 aZhang, Dingguo  uhttps://www.neurotechcenter.org/publications/2019/ieegview-open-source-multifunction-gui-based-matlab-toolbox01375nas a2200277   4500008004100000022001400041245009100055210006900146260000800215300001400223490000700237520056000244653001100804100001800815700002000833700002000853700002100873700002300894700001900917700002700936700002500963700002100988700002101009700001901030856004801049       2015                        eng d  a1525-506900aProceedings of the Seventh International Workshop on Advances in Electrocorticography.0 aProceedings of the Seventh International Workshop on Advances in  cOct  a312–3200 v513 aThe Seventh International Workshop on Advances in Electrocorticography (ECoG) convened in Washington, DC, on November 13-14, 2014. Electrocorticography-based research continues to proliferate widely across basic science and clinical disciplines. The 2014 workshop highlighted advances in neurolinguistics, brain-computer interface, functional mapping, and seizure termination facilitated by advances in the recording and analysis of the ECoG signal. The following proceedings document summarizes the content of this successful multidisciplinary gathering.10aHumans1 aRitaccio, A L1 aMatsumoto, Riki1 aMorrell, Martha1 aKamada, Kyousuke1 aKoubeissi, Mohamad1 aPoeppel, David1 aLachaux, Jean-Philippe1 aYanagisawa, Yakufumi1 aHirata, Masayuki1 aGuger, Christoph1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2632259402171nas a2200289   4500008004100000022001400041245012100055210006900176260001200245300001100257490000700268520128300275653001501558653003001573653001401603653002101617653002701638653001601665653001101681653002201692653003201714100002001746700002201766700002101788700002401809856004801833       2015                        eng d  a1532-821X00aToward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users.0 aToward independent home use of braincomputer interfaces a decisi  c03/2015  aS27-320 v963 a<p>Noninvasive brain-computer interfaces (BCIs) use scalp-recorded electrical activity from the brain to control an application. Over the past 20 years, research demonstrating that BCIs can provide communication and control to individuals with severe motor impairment has increased almost exponentially. Although considerable effort has been dedicated to offline analysis for improving signal detection and translation, far less effort has been made to conduct online studies with target populations. Thus, there remains a great need for both long-term and translational BCI studies that include individuals with disabilities in their own homes. Completing these studies is the only sure means to answer questions about BCI utility and reliability. Here we suggest an algorithm for candidate selection for electroencephalographic (EEG)-based BCI home studies. This algorithm takes into account BCI end-users and their environment and should assist in study design and substantially improve subject retention rates, thereby improving the overall efficacy of BCI home studies. It is the result of a workshop at the Fifth International BCI Meeting that allowed us to leverage the expertise of multiple research laboratories and people from multiple backgrounds in BCI research.</p>10aAlgorithms10abrain-computer interfaces10aCognition10aDisabled Persons10aElectroencephalography10aEnvironment10aHumans10aPatient Selection10aPhysical Therapy Modalities1 aKübler, Andrea1 aHolz, Elisa, Mira1 aSellers, Eric, W1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/2572154402427nas a2200349   4500008004100000022001400041245011100055210006900166260001200235300001100247490000700258520132200265653001501587653002801602653003001630653003701660653002701697653002901724653001101753653001601764653001701780653001301797653003501810653003101845653003201876653004101908653002901949100001201978700001301990700002602003856004802029       2014                        eng d  a1558-021000aAdaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.0 aAdaptive spatiotemporal filtering for movement related potential  c07/2014  a847-570 v223 aMovement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.10aAlgorithms10aArtificial Intelligence10abrain-computer interfaces10aData Interpretation, Statistical10aElectroencephalography10aEvoked Potentials, Motor10aHumans10aImagination10aMotor Cortex10aMovement10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity10aSignal Processing, Computer-Assisted10aSpatio-Temporal Analysis1 aLu, Jun1 aXie, Kan1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/2472363202595nas a2200421   4500008004100000022001400041245009200055210006900147260001200216300001100228490000700239520137400246653001001620653000901630653003401639653002801673653003001701653002801731653002701759653003501786653001101821653001101832653000901843653001601852653001901868653002301887653002801910653001801938100002001956700002101976700002601997700001902023700001902042700001902061700002102080700002402101856004802125       2014                        eng d  a2167-922300aBrain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.0 aBraincomputer interface BCI evaluation in people with amyotrophi  c06/2014  a207-150 v153 aBrain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors affecting BCI performance. Twenty-five individuals with ALS completed an evaluation protocol using a standard 6 × 6 matrix and parameters selected by stepwise linear discrimination. With an 8-channel EEG montage, the subjects fell into two groups in BCI accuracy (chance accuracy 3%). Seventeen averaged 92 (± 3)% (range 71-100%), which is adequate for communication (G70 group). Eight averaged 12 (± 6)% (range 0-36%), inadequate for communication (L40 subject group). Performance did not correlate with disability: 11/17 (65%) of G70 subjects were severely disabled (i.e. ALSFRS-R < 5). All L40 subjects had visual impairments (e.g. nystagmus, diplopia, ptosis). P300 was larger and more anterior in G70 subjects. A 16-channel montage did not significantly improve accuracy. In conclusion, most people severely disabled by ALS could use a visual P300-based BCI for communication. In those who could not, visual impairment was the principal obstacle. For these individuals, auditory P300-based BCIs might be effective.10aAdult10aAged10aAmyotrophic Lateral Sclerosis10aBiofeedback, Psychology10abrain-computer interfaces10aCommunication Disorders10aElectroencephalography10aEvent-Related Potentials, P30010aFemale10aHumans10aMale10aMiddle Aged10aOnline Systems10aPhotic Stimulation10aPsychomotor Performance10aReaction Time1 aMcCane, Lynn, M1 aSellers, Eric, W1 aMcFarland, Dennis, J.1 aMak, Joseph, N1 aCarmack, Steve1 aZeitlin, Debra1 aWolpaw, Jonathan1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/2455584300766nas a2200217   4500008004100000022001400041245013600055210006900191260001200260300001000272490000700282653002400289653003400313653004400347653001100391653002900402653002000431100002600451700002300477856004800500       2014                        eng d  a2157-310700aModality specificity is the preferred method for diagnosing the auditory processing disorder (APD): response to Moore and Ferguson.0 aModality specificity is the preferred method for diagnosing the   c08/2014  a698-90 v2510aAuditory Perception10aAuditory Perceptual Disorders10aEvoked Potentials, Auditory, Brain Stem10aHumans10aNeuropsychological Tests10aPsychoacoustics1 aMcFarland, Dennis, J.1 aCacace, Anthony, T  uhttp://www.ncbi.nlm.nih.gov/pubmed/2536537300757nas a2200217   4500008004100000022001400041245012700055210006900182260001200251300001000263490000700273653002400280653003400304653004400338653001100382653002900393653002000422100002300442700002600465856004800491       2014                        eng d  a2157-310700aModality Specificity trumps other methods for diagnosing the auditory processing disorder (APD): response to Dillon et al.0 aModality Specificity trumps other methods for diagnosing the aud  c08/2014  a703-50 v2510aAuditory Perception10aAuditory Perceptual Disorders10aEvoked Potentials, Auditory, Brain Stem10aHumans10aNeuropsychological Tests10aPsychoacoustics1 aCacace, Anthony, T1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/2536537503178nas a2200421   4500008004100000022001400041245009800055210006900153260001200222300001100234490000700245520196800252653001002220653000902230653001502239653002402254653003002278653003602308653002702344653002102371653003102392653001102423653001102434653000902445653002402454653001602478653001702494653002202511653002802533100002502561700001602586700001902602700002002621700002202641700002102663700002402684856004802708       2014                        eng d  a1741-255200aA practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening.0 apractical intuitive braincomputer interface for communicating ye  c06/2014  a0350030 v113 aOBJECTIVE:
Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words 'yes' and 'no') without loss of performance, and whether the system could be used by people in the locked-in state.
APPROACH:
We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions.
MAIN RESULTS:
The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects' performance with word stimuli (mean 77% ± 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73% ± 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects.
SIGNIFICANCE:
Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in.10aAdult10aAged10aAlgorithms10aAuditory Perception10abrain-computer interfaces10aCommunication Aids for Disabled10aElectroencephalography10aEquipment Design10aEquipment Failure Analysis10aFemale10aHumans10aMale10aMan-Machine Systems10aMiddle Aged10aQuadriplegia10aTreatment Outcome10aUser-Computer Interface1 aHill, Jeremy, Jeremy1 aRicci, Erin1 aHaider, Sameah1 aMcCane, Lynn, M1 aHeckman, Susan, M1 aWolpaw, Jonathan1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/2483827802881nas a2200397   4500008004100000022001400041245011100055210006900166260001200235300001100247490000700258520172500265653001501990653002002005653001802025653002002043653002502063653002702088653002402115653001102139653001102150653001302161653002902174653003202203653001102235100002602246700001902272700002602291700002402317700001902341700001902360700001802379700002302397700001502420856004802435       2014                        eng d  a1933-071500aReal-time functional mapping: potential tool for improving language outcome in pediatric epilepsy surgery.0 aRealtime functional mapping potential tool for improving languag  c09/2014  a287-950 v143 a<p>Accurate language localization expands surgical treatment options for epilepsy patients and reduces the risk of postsurgery language deficits. Electrical cortical stimulation mapping (ESM) is considered to be the clinical gold standard for language localization. While ESM affords clinically valuable results, it can be poorly tolerated by children, requires active participation and compliance, carries a risk of inducing seizures, is highly time consuming, and is labor intensive. Given these limitations, alternative and/or complementary functional localization methods such as analysis of electrocorticographic (ECoG) activity in high gamma frequency band in real time are needed to precisely identify eloquent cortex in children. In this case report, the authors examined 1) the use of real-time functional mapping (RTFM) for language localization in a high gamma frequency band derived from ECoG to guide surgery in an epileptic pediatric patient and 2) the relationship of RTFM mapping results to postsurgical language outcomes. The authors found that RTFM demonstrated relatively high sensitivity (75%) and high specificity (90%) when compared with ESM in a "next-neighbor" analysis. While overlapping with ESM in the superior temporal region, RTFM showed a few other areas of activation related to expressive language function, areas that were eventually resected during the surgery. The authors speculate that this resection may be associated with observed postsurgical expressive language deficits. With additional validation in more subjects, this finding would suggest that surgical planning and associated assessment of the risk/benefit ratio would benefit from information provided by RTFM mapping.</p>10aAdolescent10aAnticonvulsants10aBrain Mapping10aCerebral Cortex10aElectric Stimulation10aElectroencephalography10aEpilepsies, Partial10aFemale10aHumans10aLanguage10aNeuropsychological Tests10aSensitivity and Specificity10aSpeech1 aKorostenskaja, Milena1 aChen, Po-Ching1 aSalinas, Christine, M1 aWesterveld, Michael1 aBrunner, Peter1 aSchalk, Gerwin1 aCook, Jane, C1 aBaumgartner, James1 aLee, Ki, H  uhttp://www.ncbi.nlm.nih.gov/pubmed/2499581501203nas a2200277   4500008004100000022001400041245009000055210006900145260001200214300001000226490000700236520041500243653001200658653002600670653001300696653001100709653001700720653002100737653002400758653001100782653001600793653002500809100002200834700002100856856004800877       2014                        eng d  a1538-300800aThe simplest motor skill: mechanisms and applications of reflex operant conditioning.0 asimplest motor skill mechanisms and applications of reflex opera  c04/2014  a82-900 v423 aOperant conditioning protocols can change spinal reflexes gradually, which are the simplest behaviors. This article summarizes the evidence supporting two propositions: that these protocols provide excellent models for defining the substrates of learning and that they can induce and guide plasticity to help restore skills, such as locomotion, that have been impaired by spinal cord injury or other disorders.10aAnimals10aConditioning, Operant10aH-Reflex10aHumans10aMotor Skills10aMuscle, Skeletal10aNeuronal Plasticity10aReflex10aSpinal Cord10aSpinal Cord Injuries1 aThompson, Aiko, K1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2450873800719nas a2200217   4500008004100000022001400041245011900055210006900174260001200243300002600255490000800281653004400289653001100333653001800344653001100362653000900373653002200382100002600404700002300430856004800453       2012                        eng d  a1878-589100aQuestionable reliability of the speech-evoked auditory brainstem response (sABR) in typically-developing children.0 aQuestionable reliability of the speechevoked auditory brainstem   c05/2012  a1-2; author reply 3-50 v28710aEvoked Potentials, Auditory, Brain Stem10aFemale10aHearing Tests10aHumans10aMale10aSpeech Perception1 aMcFarland, Dennis, J.1 aCacace, Anthony, T  uhttp://www.ncbi.nlm.nih.gov/pubmed/2244617801835nas a2200241   4500008004100000022001400041245005400055210005200109260001200161300000900173490000600182520120500188653001201393653001001405653001601415653001101431653001301442653002801455100001801483700002501501700001901526856004801545       2012                        eng d  a2154-228700aSilent Communication: toward using brain signals.0 aSilent Communication toward using brain signals  c01/2012  a43-60 v33 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">From the 1980s movie Firefox to the more recent Avatar, popular science fiction has speculated about the possibility of a persons thoughts being read directly from his or her brain. Such braincomputer interfaces (BCIs) might allow people who are paralyzed to communicate with and control their environment, and there might also be applications in military situations wherever silent user-to-user communication is desirable. Previous studies have shown that BCI systems can use brain signals related to movements and movement imagery or attention-based character selection. Although these systems have successfully demonstrated the possibility to control devices using brain function, directly inferring which word a person intends to communicate has been elusive. A BCI using imagined speech might provide such a practical, intuitive device. Toward this goal, our studies to date addressed two scientific questions: (1) Can brain signals accurately characterize different aspects of speech? (2) Is it possible to predict spoken or imagined words or their components using brain signals?</span></p>10aAnimals10aBrain10aBrain Waves10aHumans10aMovement10aUser-Computer Interface1 aPei, Xiao-Mei1 aHill, Jeremy, Jeremy1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2234495101970nas a2200205   4500008004100000022001400041245009800055210006900153260001200222300001100234490000700245520134700252653001301599653001401612653001101626653002401637653002901661100002601690856004801716       2012                        eng d  a1744-411X00aA single g factor is not necessary to simulate positive correlations between cognitive tests.0 asingle g factor is not necessary to simulate positive correlatio  c01/2012  a378-840 v343 aIn the area of abilities testing, one issue of continued dissent is whether abilities are best conceptualized as manifestations of a single underlying general factor or as reflecting the combination of multiple traits that may be dissociable. The fact that diverse cognitive tests tend to be positively correlated has been taken as evidence for a single general ability or "g" factor. In the present study, simulations of test performance were run to evaluate the hypothesis that multiple independent abilities that affect test performance in a consistent manner will produce a positive manifold. Correlation matrices were simulated from models using either one or eight independent factors. The extent to which these factors operated in a consistent manner across tests (i.e., that a factor that facilitates performance on one test tends to facilitate performance on other tests) was manipulated by varying the mean value of the randomly selected weights. The tendency of both a single factor and eight independent factors to produce positive correlations increased as the randomly selected weights operated in a more consistent fashion. Thus the presence of a positive manifold in the correlations between diverse cognitive tests does not provide differential support for either single factor or multiple factor models of general abilities.10aAptitude10aCognition10aHumans10aModels, Theoretical10aNeuropsychological Tests1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/2226019002099nas a2200241   4500008004100000022001400041245011200055210006900167260001200236300001000248490000700258520136500265653001501630653001001645653002701655653001101682653001701693653002801710100002401738700002601762700002101788856004801809       2012                        eng d  a1873-274700aValue of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface.0 aValue of amplitude phase and coherence features for a sensorimot  c01/2012  a130-40 v873 aMeasures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined phase-locking value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of inter-channel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source.10aAlgorithms10aBrain10aElectroencephalography10aHumans10aMotor Cortex10aUser-Computer Interface1 aKrusienski, Dean, J1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2198598404066nas a2200277   4500008004100000022001400041245007100055210006900126260001200195300001100207490000700218520327200225653001003497653002003507653002703527653001103554653001103565653001603576653000903592653004103601653002803642100002703670700001803697700002503715856004803740       2011                        eng d  a1095-957200aCausal influence of gamma oscillations on the sensorimotor rhythm.0 aCausal influence of gamma oscillations on the sensorimotor rhyth  c05/2011  a837-420 v563 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Gamma oscillations of the electromagnetic field of the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;are known to be involved in a variety of cognitive processes, and are believed to be fundamental for information processing within the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. While gamma oscillations have been shown to be correlated with&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;rhythms at different frequencies, to date no empirical evidence has been presented that supports a causal influence of gamma oscillations on other&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;rhythms. In this work, we study the relation of gamma oscillations and the sensorimotor rhythm (SMR) in healthy human subjects using electroencephalography. We first demonstrate that modulation of the SMR, induced by motor imagery of either the left or right hand, is positively correlated with the power of frontal and occipital gamma oscillations, and negatively correlated with the power of centro-parietal gamma oscillations. We then demonstrate that the most simple causal structure, capable of explaining the observed correlation of gamma oscillations and the SMR, entails a causal influence of gamma oscillations on the SMR. This finding supports the fundamental role attributed to gamma oscillations for information processing within the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, and is of particular importance for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs). As modulation of the SMR is typically used in BCIs to infer a subject's intention, our findings entail that gamma oscillations have a causal influence on a subject's capability to utilize a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;for means of communication.</span></p>10aAdult10aCerebral Cortex10aElectroencephalography10aFemale10aHumans10aImagination10aMale10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aGrosse-Wentrup, Moritz1 aSchölkopf, B1 aHill, Jeremy, Jeremy  uhttp://www.ncbi.nlm.nih.gov/pubmed/2045162602734nas a2200349   4500008004100000022001400041245009000055210006900145260001200214300001100226490000600237520175300243653001001996653002902006653003702035653002802072653001102100653001102111653001602122653000902138653001302147653001302160653001002173653002802183100002302211700001402234700002502248700001802273700001802291700002702309856004802336       2011                        eng d  a1741-255200aClosing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery.0 aClosing the sensorimotor loop haptic feedback facilitates decodi  c06/2011  a0360050 v83 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The combination of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.</span></p>10aBrain10aEvoked Potentials, Motor10aEvoked Potentials, Somatosensory10aFeedback, Physiological10aFemale10aHumans10aImagination10aMale10aMovement10aRobotics10aTouch10aUser-Computer Interface1 aGomez-Rodriguez, M1 aPeters, J1 aHill, Jeremy, Jeremy1 aSchölkopf, B1 aGharabaghi, A1 aGrosse-Wentrup, Moritz  uhttp://www.ncbi.nlm.nih.gov/pubmed/2147487807041nas a2200313   4500008004100000022001400041245008200055210006900137260001200206300001100218490000600229520615100235653002806386653001006414653001806424653002706442653002106469653003106490653001106521653002406532653001306556653002806569100001906597700001506616700001306631700001606644700001906660856004806679       2011                        eng d  a1741-255200aCurrent Trends in Hardware and Software for Brain-Computer Interfaces (BCIs).0 aCurrent Trends in Hardware and Software for BrainComputer Interf  c04/2011  a0250010 v83 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">A&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) provides a non-muscular communication channel to people with and without disabilities.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;devices consist of hardware and software.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;hardware records signals from the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, either invasively or non-invasively, using a series of device components.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;software then translates these signals into device output commands and provides feedback. One may categorize different types of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;applications into the following four categories: basic&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">research</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, clinical/translational&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">research</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, consumer products, and emerging applications. These four categories use&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;hardware and software, but have different sets of requirements. For example, while basic&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">research</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;applications. The results indicate that&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;technology is in transition from isolated demonstrations to systematic&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">research</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and commercial&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">development</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. This process requires several multidisciplinary efforts, including the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">development</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;of better integrated and more robust&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;hardware and software, the definition of standardized&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, and the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">development</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">of certification, dissemination and reimbursement procedures.</span></p>10aBiofeedback, Psychology10aBrain10aBrain Mapping10aElectroencephalography10aEquipment Design10aEquipment Failure Analysis10aHumans10aMan-Machine Systems10aSoftware10aUser-Computer Interface1 aBrunner, Peter1 aBianchi, L1 aGuger, C1 aCincotti, F1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2143653602268nas a2200409   4500008004100000022001400041245011100055210006900166260001200235300001100247490000600258520108700264653001501351653001001366653001001376653001801386653002001404653003601424653003701460653003201497653002601529653002701555653001301582653001101595653002601606653001101632653000901643653001601652653001301668653002201681653002801703100001801731700002301749700001901772700001901791856004801810       2011                        eng d  a1741-255200aDecoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans.0 aDecoding vowels and consonants in spoken and imagined words usin  c08/2011  a0460280 v83 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Several stories in the popular media have speculated that it may be possible to infer from the brain which word a person is speaking or even thinking. While recent studies have demonstrated that brain signals can give detailed information about actual and imagined actions, such as different types of limb movements or spoken words, concrete experimental evidence for the possibility to 'read the mind', i.e. to interpret internally-generated speech, has been scarce. In this study, we found that it is possible to use signals recorded from the surface of the brain (electrocorticography) to discriminate the vowels and consonants embedded in spoken and in imagined words, and we defined the cortical areas that held the most information about discrimination of vowels and consonants. The results shed light on the distinct mechanisms associated with production of vowels and consonants, and could provide the basis for brain-based communication using imagined speech.</span></p>10aAdolescent10aAdult10aBrain10aBrain Mapping10aCerebral Cortex10aCommunication Aids for Disabled10aData Interpretation, Statistical10aDiscrimination (Psychology)10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aFunctional Laterality10aHumans10aMale10aMiddle Aged10aMovement10aSpeech Perception10aUser-Computer Interface1 aPei, Xiao-Mei1 aBarbour, Dennis, L1 aLeuthardt, E C1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2175036903784nas a2200337   4500008004100000022001400041245006200055210006100117260001200178300001100190490000700201520283200208653001503040653001003055653002403065653002903089653001103118653002603129653001103155653000903166653002903175653002103204653003103225653003403256653002203290653001603312100002103328700002603349700002303375856004803398       2011                        eng d  a1050-054500aDichotic and dichoptic digit perception in normal adults.0 aDichotic and dichoptic digit perception in normal adults  c06/2011  a332-410 v223 aBACKGROUND:
Verbally based dichotic-listening experiments and reproduction-mediated response-selection strategies have been used for over four decades to study perceptual/cognitive aspects of auditory information processing and make inferences about hemispheric asymmetries and language lateralization in the brain. Test procedures using dichotic digits have also been used to assess for disorders of auditory processing. However, with this application, limitations exist and paradigms need to be developed to improve specificity of the diagnosis. Use of matched tasks in multiple sensory modalities is a logical approach to address this issue. Herein, we use dichotic listening and dichoptic viewing of visually presented digits for making this comparison.
PURPOSE:
To evaluate methodological issues involved in using matched tasks of dichotic listening and dichoptic viewing in normal adults.
RESEARCH DESIGN:
A multivariate assessment of the effects of modality (auditory vs. visual), digit-span length (1-3 pairs), response selection (recognition vs. reproduction), and ear/visual hemifield of presentation (left vs. right) on dichotic and dichoptic digit perception.
STUDY SAMPLE:
Thirty adults (12 males, 18 females) ranging in age from 18 to 30 yr with normal hearing sensitivity and normal or corrected-to-normal visual acuity.
DATA COLLECTION AND ANALYSIS:
A computerized, custom-designed program was used for all data collection and analysis. A four-way repeated measures analysis of variance (ANOVA) evaluated the effects of modality, digit-span length, response selection, and ear/visual field of presentation.
RESULTS:
The ANOVA revealed that performances on dichotic listening and dichoptic viewing tasks were dependent on complex interactions between modality, digit-span length, response selection, and ear/visual hemifield of presentation. Correlation analysis suggested a common effect on overall accuracy of performance but isolated only an auditory factor for a laterality index.
CONCLUSIONS:
The variables used in this experiment affected performances in the auditory modality to a greater extent than in the visual modality. The right-ear advantage observed in the dichotic-digits task was most evident when reproduction mediated response selection was used in conjunction with three-digit pairs. This effect implies that factors such as "speech related output mechanisms" and digit-span length (working memory) contribute to laterality effects in dichotic listening performance with traditional paradigms. Thus, the use of multiple-digit pairs to avoid ceiling effects and the application of verbal reproduction as a means of response selection may accentuate the role of nonperceptual factors in performance. Ideally, tests of perceptual abilities should be relatively free of such effects.10aAdolescent10aAdult10aAuditory Perception10aDichotic Listening Tests10aFemale10aFunctional Laterality10aHumans10aMale10aRecognition (Psychology)10aReference Values10aReproducibility of Results10aTask Performance and Analysis10aVisual Perception10aYoung Adult1 aLawfield, Angela1 aMcFarland, Dennis, J.1 aCacace, Anthony, T  uhttp://www.ncbi.nlm.nih.gov/pubmed/2186447102954nas a2200373   4500008004100000022001400041245008200055210006900137260001200206300001100218490000700229520184300236653002802079653002702107653002802134653002702162653002202189653003002211653001102241653001302252653002502265653002402290653001202314653004102326653002802367653001802395653002202413100001902435700001502454700002502469700002002494700001802514856004802532       2011                        eng d  a1530-888X00aA graphical model framework for decoding in the visual ERP-based BCI speller.0 agraphical model framework for decoding in the visual ERPbased BC  c01/2011  a160-820 v233 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.</span></p>10aArtificial Intelligence10aComputer User Training10aDiscrimination Learning10aElectroencephalography10aEvoked Potentials10aEvoked Potentials, Visual10aHumans10aLanguage10aModels, Neurological10aModels, Theoretical10aReading10aSignal Processing, Computer-Assisted10aUser-Computer Interface10aVisual Cortex10aVisual Perception1 aMartens, S M M1 aMooij, J M1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aSchölkopf, B  uhttp://www.ncbi.nlm.nih.gov/pubmed/2096454001351nas a2200289   4500008004100000022001400041245009800055210006900153260001200222300001200234490000700246520047500253653001100728653002500739653002300764653002500787653002000812100002100832700002600853700002000879700002600899700001900925700002000944700002300964700002600987856004801013       2011                        eng d  a1552-684400aNeurological principles and rehabilitation of action disorders: rehabilitation interventions.0 aNeurological principles and rehabilitation of action disorders r  c06/2011  a33S-43S0 v253 aThis third chapter discusses the evidence for the rehabilitation of the most common movement disorders of the upper extremity. The authors also present a framework, building on the computation, anatomy, and physiology (CAP) model, for incorporating some of the principles discussed in the 2 previous chapters by Frey et al and Sathian et al in the practice of rehabilitation and for discussing potentially helpful interventions based on emergent neuroscience principles.10aHumans10aModels, Neurological10aMovement Disorders10aRecovery of Function10aUpper Extremity1 aPomeroy, Valerie1 aAglioti, Salvatore, M1 aMark, Victor, W1 aMcFarland, Dennis, J.1 aStinear, Cathy1 aWolf, Steven, L1 aCorbetta, Maurizio1 aFitzpatrick, Susan, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/2161353603596nas a2200505   4500008004100000022001400041245010700055210006900162260001200231300001300243490000700256520217800263653002502441653001502466653001002481653002502491653001802516653001602534653002002550653002402570653002702594653001302621653002202634653001102656653001102667653000902678653001602687653002902703653002302732653002302755653001802778653002202796653001702818653001502835100002202850700001802872700002902890700002402919700002002943700001802963700002302981700001903004700001903023856004803042       2011                        eng d  a1529-240100aNonuniform high-gamma (60-500 Hz) power changes dissociate cognitive task and anatomy in human cortex.0 aNonuniform highgamma 60500 Hz power changes dissociate cognitive  c02/2011  a2091-1000 v313 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">High-gamma-band (&gt;60 Hz) power changes in cortical electrophysiology are a reliable indicator of focal, event-related cortical activity. Despite discoveries of oscillatory subthreshold and synchronous suprathreshold activity at the cellular level, there is an increasingly popular view that high-gamma-band amplitude changes recorded from cellular ensembles are the result of asynchronous firing activity that yields wideband and uniform power increases. Others have demonstrated independence of power changes in the low- and high-gamma bands, but to date, no studies have shown evidence of any such independence above 60 Hz. Based on nonuniformities in time-frequency analyses of electrocorticographic (ECoG) signals, we hypothesized that induced high-gamma-band (60-500 Hz) power changes are more heterogeneous than currently understood. Using single-word repetition tasks in six human subjects, we showed that functional responsiveness of different ECoG high-gamma sub-bands can discriminate cognitive task (e.g., hearing, reading, speaking) and cortical locations. Power changes in these sub-bands of the high-gamma range are consistently present within single trials and have statistically different time courses within the trial structure. Moreover, when consolidated across all subjects within three task-relevant anatomic regions (sensorimotor, Broca's area, and superior temporal gyrus), these behavior- and location-dependent power changes evidenced nonuniform&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">trends</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;across the population. Together, the independence and nonuniformity of power changes across a broad range of frequencies suggest that a new approach to evaluating high-gamma-band cortical activity is necessary. These findings show that in addition to time and location, frequency is another fundamental dimension of high-gamma dynamics.</span></p>10aAcoustic Stimulation10aAdolescent10aAdult10aAnalysis of Variance10aBrain Mapping10aBrain Waves10aCerebral Cortex10aCognition Disorders10aElectroencephalography10aEpilepsy10aEvoked Potentials10aFemale10aHumans10aMale10aMiddle Aged10aNeuropsychological Tests10aNonlinear Dynamics10aPhotic Stimulation10aReaction Time10aSpectrum Analysis10aTime Factors10aVocabulary1 aGaona, Charles, M1 aSharma, Mohit1 aFreudenberg, Zachary, V.1 aBreshears, Jonathan1 aBundy, David, T1 aRoland, Jarod1 aBarbour, Dennis, L1 aSchalk, Gerwin1 aLeuthardt, E C  uhttp://www.ncbi.nlm.nih.gov/pubmed/2130724602106nas a2200421   4500008004100000022001400041245009000055210006900145260001200214300001100226490000700237520091200244653001001156653001801166653001601184653003301200653002701233653001301260653001101273653001801284653002801302100001801330700002401348700001901372700002701391700002001418700002201438700002001460700002301480700001601503700002001519700001301539700001901552700002201571700002401593700001901617856004801636       2011                        eng d  a1525-506900aProceedings of the Second International Workshop on Advances in Electrocorticography.0 aProceedings of the Second International Workshop on Advances in   c12/2011  a641-500 v223 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The Second International Workshop on Advances in Electrocorticography (ECoG) was convened in San Diego, CA, USA, on November 11-12, 2010. Between this meeting and the inaugural 2009 event, a much clearer picture has been emerging of cortical ECoG physiology and its relationship to local field potentials and single-cell recordings. Innovations in material engineering are advancing the goal of a stable long-term recording interface. Continued evolution of ECoG-driven brain-computer interface technology is determining innovation in neuroprosthetics. Improvements in instrumentation and statistical methodologies continue to elucidate ECoG correlates of normal human function as well as the ictal state. This proceedings document summarizes the current status of this rapidly evolving field.</span></p>10aBrain10aBrain Mapping10aBrain Waves10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aHumans10aUnited States10aUser-Computer Interface1 aRitaccio, A L1 aBoatman-Reich, Dana1 aBrunner, Peter1 aCervenka, Mackenzie, C1 aCole, Andrew, J1 aCrone, Nathan, E.1 aDuckrow, Robert1 aKorzeniewska, Anna1 aLitt, Brian1 aMiller, John, W1 aMoran, D1 aParvizi, Josef1 aViventi, Jonathan1 aWilliams, Justin, C1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2203628703279nas a2200337   4500008004100000022001400041245011400055210006900169260001200238300001200250490000700262520231800269653001502587653001002602653001002612653001802622653002702640653001102667653001102678653000902689653001602698653004102714653002002755100001802775700001902793700002202812700001902834700002102853700001902874856004802893       2011                        eng d  a1095-957200aSpatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition.0 aSpatiotemporal dynamics of electrocorticographic high gamma acti  c02/2011  a2960-720 v543 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Language is one of the defining abilities of humans. Many studies have characterized the neural correlates of different aspects of language processing. However, the imaging techniques typically used in these studies were limited in either their temporal or spatial resolution. Electrocorticographic (ECoG) recordings from the surface of the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;combine high spatial with high temporal resolution and thus could be a valuable tool for the study of neural correlates of language function. In this study, we defined the spatiotemporal dynamics of ECoG activity during a word repetition task in nine human subjects. ECoG was recorded while each subject overtly or covertly repeated words that were presented either visually or auditorily. ECoG amplitudes in the high gamma (HG) band confidently tracked neural changes associated with stimulus presentation and with the subject's verbal response. Overt word production was primarily associated with HG changes in the superior and middle parts of temporal lobe, Wernicke's area, the supramarginal gyrus, Broca's area, premotor cortex (PMC), primary motor cortex. Covert word production was primarily associated with HG changes in superior temporal lobe and the supramarginal gyrus. Acoustic processing from both auditory stimuli as well as the subject's own voice resulted in HG power changes in superior temporal lobe and Wernicke's area. In summary, this study represents a comprehensive characterization of overt and covert speech using electrophysiological imaging with high spatial and temporal resolution. It thereby complements the findings of previous neuroimaging studies of language and thus further adds to&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">current</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;understanding of word processing in humans.</span></p>10aAdolescent10aAdult10aBrain10aBrain Mapping10aElectroencephalography10aFemale10aHumans10aMale10aMiddle Aged10aSignal Processing, Computer-Assisted10aVerbal Behavior1 aPei, Xiao-Mei1 aLeuthardt, E C1 aGaona, Charles, M1 aBrunner, Peter1 aWolpaw, Jonathan1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2102978400601nas a2200205   4500008004100000022001400041245007100055210006800126260001200194300001100206490000800217653001400225653001000239653002100249653001100270653002800281100001900309700001900328856004800347       2011                        eng d  a1872-895200aToward a gaze-independent matrix speller brain-computer interface.0 aToward a gazeindependent matrix speller braincomputer interface  c06/2011  a1063-40 v12210aAttention10aBrain10aFixation, Ocular10aHumans10aUser-Computer Interface1 aBrunner, Peter1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2118340404388nas a2200385   4500008004100000022001400041245009500055210006900150260001200219300001100231490000800242520325500250653001003505653003403515653002103549653001003570653003603580653002403616653002703640653002103667653001103688653000903699653004103708653002803749100002603777700002503803700001403828700001903842700001403861700001503875700002503890700002103915700001803936856004803954       2011                        eng d  a1872-895200aTransition from the locked in to the completely locked-in state: a physiological analysis.0 aTransition from the locked in to the completely lockedin state a  c06/2011  a925-330 v1223 a<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure&nbsp;<span class="highlight">brain-computer-interface</span>&nbsp;(<span class="highlight">BCI</span>) communication.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related&nbsp;<span class="highlight">brain</span>potentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSIONS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">SIGNIFICANCE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Auditory and proprioceptive&nbsp;<span class="highlight">brain-computer-interface</span>&nbsp;(<span class="highlight">BCI</span>) systems are the only remaining communication channels in CLIS.</p>10aAdult10aAmyotrophic Lateral Sclerosis10aArea Under Curve10aBrain10aCommunication Aids for Disabled10aDisease Progression10aElectroencephalography10aElectromyography10aHumans10aMale10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aMurguialday, Ramos, A1 aHill, Jeremy, Jeremy1 aBensch, M1 aMartens, S M M1 aHalder, S1 aNijboer, F1 aSchoelkopf, Bernhard1 aBirbaumer, Niels1 aGharabaghi, A  uhttp://www.ncbi.nlm.nih.gov/pubmed/2088829203803nas a2200433   4500008004100000022001400041024002500055245010000080210006900180260001200249300001100261490000600272520257600278653001002854653001002864653001802874653002502892653002702917653002202944653002802966653001102994653001103005653001603016653000903032653001603041653001403057653003403071653002803105100001903133700002203152700001803174700002103192700001803213700002903231700001803260700002403278700001903302856004803321       2011                        eng d  a1741-2552  aNIHMSID: NIHMS48176700aUsing the electrocorticographic speech network to control a brain-computer interface in humans.0 aUsing the electrocorticographic speech network to control a brai  c06/2011  a0360040 v83 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocorticography (ECoG) has emerged as a new signal platform for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;operation and that these signals can be acquired from a cortical array that is small and minimally invasive.</span></p>10aAdult10aBrain10aBrain Mapping10aComputer Peripherals10aElectroencephalography10aEvoked Potentials10aFeedback, Physiological10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aNerve Net10aSpeech Production Measurement10aUser-Computer Interface1 aLeuthardt, E C1 aGaona, Charles, M1 aSharma, Mohit1 aSzrama, Nicholas1 aRoland, Jarod1 aFreudenberg, Zachary, V.1 aSolisb, Jamie1 aBreshears, Jonathan1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2147163804192nas a2200409   4500008004100000022001400041245005900055210005700114260001200171300001100183490000700194520303500201653001403236653001403250653001503264653002703279653001303306653001103319653004003330653003103370653002903401653001103430653002203441653002803463653002203491653001703513653002803530100002803558700001703586700002303603700002503626700001903651700002003670700002403690700002003714856004803734       2010                        eng d  a1531-824900aBrain-computer interfacing based on cognitive control.0 aBraincomputer interfacing based on cognitive control  c06/2010  a809-160 v673 a<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces (BCIs) translate deliberate intentions and associated changes in brain activity into action, thereby offering patients with severe paralysis an alternative means of communication with and control over their environment. Such systems are not available yet, partly due to the high performance standard that is required. A major challenge in the development of implantable BCIs is to identify cortical regions and related functions that an individual can reliably and consciously manipulate. Research predominantly focuses on the sensorimotor cortex, which can be activated by imagining motor actions. However, because this region may not provide an optimal solution to all patients, other neuronal networks need to be examined. Therefore, we investigated whether the cognitive control network can be used for BCI purposes. We also determined the feasibility of using functional magnetic resonance imaging (fMRI) for noninvasive localization of the cognitive control network.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Three patients with intractable epilepsy, who were temporarily implanted with subdural grid electrodes for diagnostic purposes, attempted to gain BCI control using the electrocorticographic (ECoG) signal of the left dorsolateral prefrontal cortex (DLPFC).</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">All subjects quickly gained accurate BCI control by modulation of gamma-power of the left DLPFC. Prelocalization of the relevant region was performed with fMRI and was confirmed using the ECoG signals obtained during mental calculation localizer tasks.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">INTERPRETATION:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The results indicate that the cognitive control network is a suitable source of signals for BCI applications. They also demonstrate the feasibility of translating understanding about cognitive networks derived from functional neuroimaging into clinical applications.</p>10aCognition10aComputers10aElectrodes10aElectroencephalography10aEpilepsy10aHumans10aImage Processing, Computer-Assisted10aMagnetic Resonance Imaging10aNeuropsychological Tests10aOxygen10aPrefrontal Cortex10aPsychomotor Performance10aSpectrum Analysis10aTime Factors10aUser-Computer Interface1 aVansteensel, Mariska, J1 aHermes, Dora1 aAarnoutse, Erik, J1 aBleichner, Martin, G1 aSchalk, Gerwin1 aRijen, Peter, C1 aLeijten, Frans, S S1 aRamsey, Nick, F  uhttp://www.ncbi.nlm.nih.gov/pubmed/2051794307936nas a2200361   4500008004100000022001400041245009600055210006900151260001200220300001100232490000800243520694900251653001507200653001007215653002807225653002007253653001007273653002507283653002407308653001107332653001107343653000907354653001607363653001907379653001607398100001607414700001907430700002207449700001707471700001707488700002107505856004807526       2010                        eng d  a1091-649000aCortical activity during motor execution, motor imagery, and imagery-based online feedback.0 aCortical activity during motor execution motor imagery and image  c03/2010  a4430-50 v1073 a<p><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Imagery</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;movement plays an important role in learning of complex&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;skills, from learning to serve in tennis to perfecting a pirouette in ballet. What and where are the neural substrates that underlie&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">imagery-based</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;learning? We measured electrocorticographic&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">cortical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;surface potentials in eight human subjects during overt action and kinesthetic&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">imagery</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;of the same movement, focusing on power in "high frequency" (76-100 Hz) and "low frequency" (8-32 Hz) ranges. We quantitatively establish that the spatial distribution of local neuronal population&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">activity</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;during&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">imagery</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;mimics the spatial distribution of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">activity</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;during actual&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;movement. By comparing responses to electrocortical stimulation with&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">imagery</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">-induced&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">cortical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;surface&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">activity</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, we demonstrate the role of primary&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;areas in movement&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">imagery</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. The magnitude of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">imagery</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">-induced&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">cortical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">activity</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;change was approximately 25% of that associated with actual movement. However, when subjects learned to use this&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">imagery</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;to control a computer cursor in a simple&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">feedback</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;task, the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">imagery</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">-induced&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">activity</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;change was significantly augmented, even exceeding that of overt movement.</span></p>10aAdolescent10aAdult10aBiofeedback, Psychology10aCerebral Cortex10aChild10aElectric Stimulation10aElectrocardiography10aFemale10aHumans10aMale10aMiddle Aged10aMotor Activity10aYoung Adult1 aMiller, K J1 aSchalk, Gerwin1 aFetz, Eberhard, E1 aNijs, Marcel1 aOjemann, J G1 aRao, Rajesh, P N  uhttp://www.ncbi.nlm.nih.gov/pubmed/2016008402830nas a2200337   4500008004100000022001400041245004900055210004500104260001200149300001100161490000600172520196200178653001002140653003502150653001802185653001102203653001102214653000902225653001602234653002502250653002302275653002802298653001602326100001902342700001302361700001502374700002102389700001502410700001902425856004802444       2010                        eng d  a1741-255200aDoes the 'P300' speller depend on eye gaze?.0 aDoes the P300 speller depend on eye gaze  c10/2010  a0560130 v73 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain-computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called 'P300 matrix speller' that was first described by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). It has been widely assumed that this method does not depend on the ability to focus on the desired character, because it was thought that it relies primarily on the P300-evoked potential and minimally, if at all, on other EEG features such as the visual-evoked potential (VEP). This issue is highly relevant for the clinical application of this BCI method, because eye movements may be impaired or lost in the relevant user population. This study investigated the extent to which the performance in a 'P300' speller BCI depends on eye gaze. We evaluated the performance of 17 healthy subjects using a 'P300' matrix speller under two conditions. Under one condition ('letter'), the subjects focused their eye gaze on the intended letter, while under the second condition ('center'), the subjects focused their eye gaze on a fixation cross that was located in the center of the matrix. The results show that the performance of the 'P300' matrix speller in normal subjects depends in considerable measure on gaze direction. They thereby disprove a widespread assumption in BCI research, and suggest that this BCI might function more effectively for people who retain some eye-movement control. The applicability of these findings to people with severe neuromuscular disabilities (particularly in eye-movements) remains to be determined.</span></p>10aAdult10aEvent-Related Potentials, P30010aEye Movements10aFemale10aHumans10aMale10aMiddle Aged10aModels, Neurological10aPhotic Stimulation10aUser-Computer Interface10aYoung Adult1 aBrunner, Peter1 aJoshi, S1 aBriskin, S1 aWolpaw, Jonathan1 aBischof, H1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2085892404454nas a2200433   4500008004100000022001400041245010500055210006900160260001200229300001100241490000700252520325900259653002503518653001503543653001003558653001803568653002003586653002803606653002703634653001303661653001103674653001103685653000903696653002203705653001603727653002303743653001103766653002003777653001603797100001603813700002303829700001903852700001803871700001803889700002403907700002203931700001903953856004803972       2010                        eng d  a1524-404000aElectrocorticographic frequency alteration mapping for extraoperative localization of speech cortex.0 aElectrocorticographic frequency alteration mapping for extraoper  c02/2010  aE407-90 v663 a<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocortical stimulation (ECS) has long been established for delineating eloquent cortex in extraoperative mapping. However, ECS is still coarse and inefficient in delineating regions of functional cortex and can be hampered by afterdischarges. Given these constraints, an adjunct approach to defining motor cortex is the use of electrocorticographic (ECoG) signal changes associated with active regions of cortex. The broad range of frequency oscillations are categorized into 2 main groups with respect to sensorimotor cortex: low-frequency bands (LFBs) and high-frequency bands (HFBs). The LFBs tend to show a power reduction, whereas the HFBs show power increases with cortical activation. These power changes associated with activated cortex could potentially provide a powerful tool in delineating areas of speech cortex. We explore ECoG signal alterations as they occur with activated region of speech cortex and its potential in clinical brain mapping applications.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We evaluated 7 patients who underwent invasive monitoring for seizure localization. Each had extraoperative ECS mapping to identify speech cortex. Additionally, all subjects performed overt speech tasks with an auditory or a visual cue to identify associated frequency power changes in regard to location and degree of concordance with ECS results.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocorticographic frequency alteration mapping (EFAM) had an 83.9% sensitivity and a 40.4% specificity in identifying any language site when considering both frequency bands and both stimulus cues. Electrocorticographic frequency alteration mapping was more sensitive in identifying the Wernicke area (100%) than the Broca area (72.2%). The HFB is uniquely suited to identifying the Wernicke area, whereas a combination of the HFB and LFB is important for Broca localization.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSION:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The concordance between stimulation and spectral power changes demonstrates the possible utility of EFAM as an adjunct method to improve the efficiency and resolution of identifying speech cortex.</p>10aAcoustic Stimulation10aAdolescent10aAdult10aBrain Mapping10aCerebral Cortex10aChi-Square Distribution10aElectroencephalography10aEpilepsy10aFemale10aHumans10aMale10aMass Spectrometry10aMiddle Aged10aPhotic Stimulation10aSpeech10aVerbal Behavior10aYoung Adult1 aWu, Melinda1 aWisneski, Kimberly1 aSchalk, Gerwin1 aSharma, Mohit1 aRoland, Jarod1 aBreshears, Jonathan1 aGaona, Charles, M1 aLeuthardt, E C  uhttp://www.ncbi.nlm.nih.gov/pubmed/2008711103481nas a2200289   4500008004100000022001400041245009200055210006900147260001200216300001000228490000700238520264000245653001802885653002002903653002002923653001502943653002502958653002702983653001103010653002703021100001803048700001903066700002003085700001903105700001903124856004803143       2010                        eng d  a1525-506900aPassive real-time identification of speech and motor cortex during an awake craniotomy.0 aPassive realtime identification of speech and motor cortex durin  c05/2010  a123-80 v183 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Precise localization of eloquent cortex is a clinical necessity prior to surgical resections adjacent to speech or&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;cortex. In the intraoperative setting, this traditionally requires inducing temporary lesions by direct electrocortical stimulation (DECS). In an attempt to increase efficiency and potentially reduce the amount of necessary stimulation, we used a passive mapping procedure in the setting of an awake craniotomy for tumor in two patients resection. We recorded electrocorticographic (ECoG) signals from exposed cortex while patients performed simple cue-directed&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and speech tasks. SIGFRIED, a procedure for real-time event detection, was used to identify areas of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">cortical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;activation by detecting task-related modulations in the ECoG high gamma band. SIGFRIED's real-time output quickly localized&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and speech areas of cortex similar to those identified by DECS. In conclusion, real-time passive identification of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">cortical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;function using SIGFRIED may serve as a useful adjunct to&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">cortical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;stimulation mapping in the intraoperative setting.</span></p>10aBrain Mapping10aBrain Neoplasms10aCerebral Cortex10aCraniotomy10aElectric Stimulation10aElectroencephalography10aHumans10aNeurologic Examination1 aRoland, Jarod1 aBrunner, Peter1 aJohnston, James1 aSchalk, Gerwin1 aLeuthardt, E C  uhttp://www.ncbi.nlm.nih.gov/pubmed/2047874501606nas a2200301   4500008004100000022001400041245007000055210006600125260001200191300001200203490000700215520071500222653001000937653002100947653002700968653002200995653001101017653002501028653003101053653004101084653001701125653002801142100002001170700002301190700001901213700002401232856004801256       2010                        eng d  a1558-253100aA procedure for measuring latencies in brain-computer interfaces.0 aprocedure for measuring latencies in braincomputer interfaces  c06/2010  a1785-970 v573 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware and software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration.</span></p>10aBrain10aComputer Systems10aElectroencephalography10aEvoked Potentials10aHumans10aModels, Neurological10aReproducibility of Results10aSignal Processing, Computer-Assisted10aTime Factors10aUser-Computer Interface1 aWilson, Adam, J1 aMellinger, Jürgen1 aSchalk, Gerwin1 aWilliams, Justin, C  uhttp://www.ncbi.nlm.nih.gov/pubmed/2040378101656nas a2200337   4500008004100000022001400041245008900055210006900144260001200213300001100225490000700236520066900243653001000912653001800922653003300940653002700973653001101000653003001011653001301041653003601054100001801090700001901108700002701127700002201154700001301176700001901189700002301208700002001231700001901251856004801270       2010                        eng d  a1525-506900aProceedings of the first international workshop on advances in electrocorticography.0 aProceedings of the first international workshop on advances in e  c10/2010  a204-150 v193 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">In October 2009, a group of neurologists, neurosurgeons, computational neuroscientists, and engineers congregated to present novel developments transforming human electrocorticography (ECoG) beyond its established relevance in clinical epileptology. The contents of the proceedings advanced the role of ECoG in seizure detection and prediction, neurobehavioral research, functional mapping, and brain-computer interface technology. The meeting established the foundation for future work on the methodology and application of surface brain recordings.</span></p>10aBrain10aBrain Mapping10aDiagnosis, Computer-Assisted10aElectroencephalography10aHumans10aInternational Cooperation10aSeizures10aSignal Detection, Psychological1 aRitaccio, A L1 aBrunner, Peter1 aCervenka, Mackenzie, C1 aCrone, Nathan, E.1 aGuger, C1 aLeuthardt, E C1 aOostenveld, Robert1 aStacey, William1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2088938401127nas a2200241   4500008004100000022001400041245003400055210003300089260001200122300001000134490000700144520043600151653005800587653001100645653002900656653002100685653002600706653002000732653003700752100002600789700002200815856004800837       2010                        eng d  a1469-182500aSymptoms as latent variables.0 aSymptoms as latent variables  c06/2010  a165-60 v333 aIn the target article, Cramer et al. suggest that diagnostic classification is improved by modeling the relationship between manifest variables (i.e., symptoms) rather than modeling unobservable latent variables (i.e., diagnostic categories such as Generalized Anxiety Disorder). This commentary discusses whether symptoms represent manifest or latent variables and the implications of this distinction for diagnosis and treatment.10aDiagnostic and Statistical Manual of Mental Disorders10aHumans10aInterview, Psychological10aMental Disorders10aModels, Psychological10aSleep Disorders10aStress Disorders, Post-Traumatic1 aMcFarland, Dennis, J.1 aMalta, Loretta, S  uhttp://www.ncbi.nlm.nih.gov/pubmed/2058438402204nas a2200469   4500008004100000022001400041245010100055210006900156260001200225300000900237490000700246520081300253653001001066653002001076653001801096653001001114653002001124653003301144653002701177653001601204653001301220653001101233653003101244653003101275653002001306653001801326653001201344653001701356653001601373653003001389653001301419653003201432653004901464653002401513653003801537100002501575700001701600700002201617700002301639710002401662856004801686       2009                        eng d  a1525-506900aAdvances in the application of technology to epilepsy: the CIMIT/NIO Epilepsy Innovation Summit.0 aAdvances in the application of technology to epilepsy the CIMITN  c09/2009  a3-460 v163 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">In 2008, a group of clinicians, scientists, engineers, and industry representatives met to discuss advances in the application of engineering technologies to the diagnosis and treatment of patients with epilepsy. The presentations also provided a guide for further technological development, specifically in the evaluation of patients for epilepsy surgery, seizure onset detection and seizure prediction, intracranial treatment systems, and extracranial treatment systems. This article summarizes the discussions and demonstrates that cross-disciplinary interactions can catalyze collaborations between physicians and engineers to address and solve many of the pressing unmet needs in epilepsy.</span></p>10aAdult10aAnticonvulsants10aBrain Mapping10aChild10aDrug Resistance10aElectric Stimulation Therapy10aElectroencephalography10aEngineering10aEpilepsy10aHumans10aMagnetic Resonance Imaging10aMedical Laboratory Science10aMicroelectrodes10aNanoparticles10aNeurons10aNeurosurgery10aNeurotoxins10aPredictive Value of Tests10aSeizures10aSpectroscopy, Near-Infrared10aTomography, Emission-Computed, Single-Photon10aTomography, Optical10aTranscranial Magnetic Stimulation1 aSchachter, Steven, C1 aGuttag, John1 aSchiff, Steven, J1 aSchomer, Donald, L1 aSummit Contributors  uhttp://www.ncbi.nlm.nih.gov/pubmed/1978022502146nas a2200397   4500008004100000022001400041245009000055210006900145260001200214300001100226490000600237520112800243653001501371653001001386653001701396653001001413653002101423653001301444653001101457653001201468653001101480653000901491653002001500653001601520653001901536653000901555653001001564653001701574653001601591100001601607700002001623700001701643700002101660700001901681856004801700       2009                        eng d  a1741-255200aDecoding flexion of individual fingers using electrocorticographic signals in humans.0 aDecoding flexion of individual fingers using electrocorticograph  c12/2009  a0660010 v63 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain signals can provide the basis for a non-muscular communication and control system, a brain-computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.</span></p>10aAdolescent10aAdult10aBiomechanics10aBrain10aElectrodiagnosis10aEpilepsy10aFemale10aFingers10aHumans10aMale10aMicroelectrodes10aMiddle Aged10aMotor Activity10aRest10aThumb10aTime Factors10aYoung Adult1 aKubánek, J1 aMiller, John, W1 aOjemann, J G1 aWolpaw, Jonathan1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/1979423701983nas a2200373   4500008004100000022001400041245011800055210006900173260000900242300001100251490000900262520077500271653001501046653002401061653003001085653001101115653000901126653003501135653003201170653002301202653003101225653003201256653002801288653001801316100002001334700001701354700001901371700002001390700002401410700001701434700001701451700002101468856012001489       2009                        eng d  a1557-170X00aDetection of spontaneous class-specific visual stimuli with high temporal accuracy in human electrocorticography.0 aDetection of spontaneous classspecific visual stimuli with high   c2009  a6465-80 v20093 aMost brain-computer interface classification experiments from electrical potential recordings have been focused on the identification of classes of stimuli or behavior where the timing of experimental parameters is known or pre-designated. Real world experience, however, is spontaneous, and to this end we describe an experiment predicting the occurrence, timing, and types of visual stimuli perceived by a human subject from electrocorticographic recordings. All 300 of 300 presented stimuli were correctly detected, with a temporal precision of order 20 ms. The type of stimulus (face/house) was correctly identified in 95% of these cases. There were approximately 20 false alarm events, corresponding to a late 2nd neuronal response to a previously identified event.10aAlgorithms10aElectrocardiography10aEvoked Potentials, Visual10aHumans10aMale10aPattern Recognition, Automated10aPattern Recognition, Visual10aPhotic Stimulation10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface10aVisual Cortex1 aMiller, John, W1 aHermes, Dora1 aSchalk, Gerwin1 aRamsey, Nick, F1 aJagadeesh, Bharathi1 aNijs, Marcel1 aOjemann, J G1 aRao, Rajesh, P N  uhttps://www.neurotechcenter.org/publications/2009/detection-spontaneous-class-specific-visual-stimuli-high-temporal02156nas a2200337   4500008004100000022001400041245008300055210006900138260001200207300000700219490000700226520120000233653001001433653002001443653001101463653002401474653001701498653001301515653002301528653002401551653002801575653001301603653004101616653002801657100001901685700001901704700001801723700001601741700001301757856004801770       2009                        eng d  a1092-068400aEvolution of brain-computer interfaces: going beyond classic motor physiology.0 aEvolution of braincomputer interfaces going beyond classic motor  c07/2009  aE40 v273 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.</span></p>10aBrain10aCerebral Cortex10aHumans10aMan-Machine Systems10aMotor Cortex10aMovement10aMovement Disorders10aNeuronal Plasticity10aProstheses and Implants10aResearch10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aLeuthardt, E C1 aSchalk, Gerwin1 aRoland, Jarod1 aRouse, Adam1 aMoran, D  uhttp://www.ncbi.nlm.nih.gov/pubmed/1956989202457nas a2200349   4500008004100000022001400041245012600055210006900181260001200250300001200262490000700274520141700281653001501698653001001713653001801723653002601741653002101767653001301788653002401801653000901825653001101834653001801845653001901863653003101882653002301913653004101936100002001977700002301997700002002020700001902040856004802059       2009                        eng d  a1879-278200aMapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.0 aMapping broadband electrocorticographic recordings to twodimensi  c11/2009  a1257-700 v223 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-machine interfaces (BMIs) aim to translate the motor intent of locked-in patients into neuroprosthetic control commands. Electrocorticographical (ECoG) signals provide promising neural inputs to BMIs as shown in recent studies. In this paper, we utilize a broadband spectrum above the fast gamma ranges and systematically study the role of spectral resolution, in which the broadband is partitioned, on the reconstruction of the patients' hand trajectories. Traditionally, the power of ECoG rhythms (&lt;200-300 Hz) has been computed in short duration bins and instantaneously and linearly mapped to cursor trajectories. Neither time embedding, nor nonlinear mappings have been previously implemented in ECoG neuroprosthesis. Herein, mapping of neural modulations to goal-oriented motor behavior is achieved via linear adaptive filters with embedded memory depths and as a novelty through echo state networks (ESNs), which provide nonlinear mappings without compromising training complexity or increasing the number of model parameters, with up to 85% correlation. Reconstructed hand trajectories are analyzed through spatial, spectral and temporal sensitivities. The superiority of nonlinear mappings in the cases of low spectral resolution and abundance of interictal activity is discussed.</span></p>10aAlgorithms10aBrain10aBrain Mapping10aElectrodes, Implanted10aElectrodiagnosis10aEpilepsy10aFeasibility Studies10aHand10aHumans10aLinear Models10aMotor Activity10aNeural Networks (Computer)10aNonlinear Dynamics10aSignal Processing, Computer-Assisted1 aGunduz, Aysegul1 aSanchez, Justin, C1 aCarney, Paul, R1 aPrincipe, Jose  uhttp://www.ncbi.nlm.nih.gov/pubmed/1964798101338nas a2200301   4500008004100000022001400041245003800055210003500093260001300128300001100141490000700152520054900159653001400708653001000722653001800732653002500750653002500775653001100800653002100811653003500832653001700867653002800884100001900912700001700931700002500948700001500973856004800988       2009                        eng d  a1879-278200aA note on ethical aspects of BCI.0 anote on ethical aspects of BCI  c11/2009   a1352-70 v223 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">This paper focuses on ethical aspects of BCI, as a research and a clinical tool, that are challenging for practitioners currently working in the field. Specifically, the difficulties involved in acquiring informed consent from locked-in patients are investigated, in combination with an analysis of the shared moral responsibility in BCI teams, and the complications encountered in establishing effective communication with media.</span></p>10aBioethics10aBrain10aCommunication10aCommunications Media10aCooperative Behavior10aHumans10aInformed Consent10aProfessional-Patient Relations10aQuadriplegia10aUser-Computer Interface1 aHaselager, Pim1 aVlek, Rutger1 aHill, Jeremy, Jeremy1 aNijboer, F  uhttp://www.ncbi.nlm.nih.gov/pubmed/1961640501758nas a2200361   4500008004100000022001400041245012300055210006900178260001200247300001100259490000600270520064200276653001500918653001000933653001400943653002400957653002700981653003501008653001101043653002501054653003501079653002301114653001401137653004101151653003401192653002801226653001201254100001901266700002501285700002001310700001801330856004801348       2009                        eng d  a1741-255200aOverlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.0 aOverlap and refractory effects in a braincomputer interface spel  c04/2009  a0260030 v63 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.</span></p>10aAlgorithms10aBrain10aCognition10aComputer Simulation10aElectroencephalography10aEvent-Related Potentials, P30010aHumans10aModels, Neurological10aPattern Recognition, Automated10aPhotic Stimulation10aSemantics10aSignal Processing, Computer-Assisted10aTask Performance and Analysis10aUser-Computer Interface10aWriting1 aMartens, S M M1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aSchölkopf, B  uhttp://www.ncbi.nlm.nih.gov/pubmed/1925546202116nas a2200277   4500008004100000022001400041245011300055210006900168260001200237300001500249490000700264520130300271653001001574653002701584653001201611653001101623653000901634653001901643653004101662653001601703100001801719700002001737700001801757700001501775856004801790       2009                        eng d  a1001-551500aPower spectrum analysis on the multiparameter electroencephalogram features of physiological mental fatigue.0 aPower spectrum analysis on the multiparameter electroencephalogr  c02/2009  a162-6, 1720 v263 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The aim of this experiment is to find a feasible impersonal index for analyzing the physiological mental fatigue level. Three characteristic parameters, relative power in different rhythm, barycenter frequency and power spectral entropy, are extracted from two channels' electroencephalogram (EEG) under two physiological mental fatigue states. Then relationships between such three parameters and physiological mental fatigue are analyzed to explore whether they can be of use for detecting (or monitoring) the mental fatigue level. The experiment results show that the relative power, barycenter frequency and power spectral entropy of EEG exhibit strong correlation with physiological mental fatigue level. While physiological mental fatigue level increases, the relative power in theta, alpha and beta rhythms, barycenter frequency and power spectral entropy of EEG decrease, but the relative power in delta rhythm of EEG increases. The relative power in four rhythms, barycenter frequency and power spectral entropy of EEG reflect the change of physiological mental fatigue level sensitively, and may hopefully be used as indexes for detecting physiological mental fatigue level.</span></p>10aAdult10aElectroencephalography10aEntropy10aHumans10aMale10aMental Fatigue10aSignal Processing, Computer-Assisted10aYoung Adult1 aZhang, Ai-hua1 aZheng, Shi Dong1 aPei, Xiao-Mei1 aOuyang, Yi  uhttp://www.ncbi.nlm.nih.gov/pubmed/1933457702591nas a2200433   4500008004100000022001400041245012500055210006900180260001200249300001100261490000700272520133300279653001001612653001801622653002001640653002501660653002601685653002701711653001301738653001101751653001101762653000901773653001601782653003301798653004101831653001601872100001901888700001801907700002201925700002201947700002001969700002401989700002302013700002002036700001902056700001502075700001902090856004802109       2009                        eng d  a1525-506900aA practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans.0 apractical procedure for realtime functional mapping of eloquent   c07/2009  a278-860 v153 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Functional mapping of eloquent cortex is often necessary prior to invasive brain surgery, but current techniques that derive this mapping have important limitations. In this article, we demonstrate the first comprehensive evaluation of a rapid, robust, and practical mapping system that uses passive recordings of electrocorticographic signals. This mapping procedure is based on the BCI2000 and SIGFRIED technologies that we have been developing over the past several years. In our study, we evaluated 10 patients with epilepsy from four different institutions and compared the results of our procedure with the results derived using electrical cortical stimulation (ECS) mapping. The results show that our procedure derives a functional motor cortical map in only a few minutes. They also show a substantial concurrence with the results derived using ECS mapping. Specifically, compared with ECS maps, a next-neighbor evaluation showed no false negatives, and only 0.46 and 1.10% false positives for hand and tongue maps, respectively. In summary, we demonstrate the first comprehensive evaluation of a practical and robust mapping procedure that could become a new tool for planning of invasive brain surgeries.</span></p>10aAdult10aBrain Mapping10aCerebral Cortex10aElectric Stimulation10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aHumans10aMale10aMiddle Aged10aPractice Guidelines as Topic10aSignal Processing, Computer-Assisted10aYoung Adult1 aBrunner, Peter1 aRitaccio, A L1 aLynch, Timothy, M1 aEmrich, Joseph, F1 aWilson, Adam, J1 aWilliams, Justin, C1 aAarnoutse, Erik, J1 aRamsey, Nick, F1 aLeuthardt, E C1 aBischof, H1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/1936663801698nas a2200265   4500008004100000022001400041245008600055210006900141260001200210300000900222490000900231520087600240653001501116653002801131653003301159653002701192653001301219653001101232653003501243653003101278653003201309100002601341700001701367856004801384       2009                        eng d  a1557-170X00aSeizure prediction for epilepsy using a multi-stage phase synchrony based system.0 aSeizure prediction for epilepsy using a multistage phase synchro  c09/2009  a25-80 v20093 aSeizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques. Here we present a multi-stage phase synchrony based system that brings to bear the advantages of many techniques in each substage. The 1(st) stage of the system unmixes continuous long-term (2-4 days) multichannel scalp EEG using spatially constrained Independent Component Analysis and estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. It then projects multidimensional features onto a 2-D map using Neuroscale and evaluates the probability of predictive events using Gaussian Mixture Models. We show the possibility of seizure onset prediction within a prediction window of 35-65 minutes with a sensitivity of 65-100% and specificity of 65-80% across epileptic patients.10aAlgorithms10aArtificial Intelligence10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aHumans10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity1 aJames, Christopher, J1 aGupta, Disha  uhttp://www.ncbi.nlm.nih.gov/pubmed/1996510402198nas a2200229   4500008004100000022001400041245009000055210006900145260001200214520150500226653001001731653001601741653001501757653002701772653001101799653002801810100002001838700001901858700001901877700002401896856004801920       2009                        eng d  a1940-087X00aUsing an EEG-based brain-computer interface for virtual cursor movement with BCI2000.0 aUsing an EEGbased braincomputer interface for virtual cursor mov  c07/20093 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features that change with the imagined movements. The results from this calibration will show the best channels to use, which are configured so that amplitude changes in the mu and beta frequency bands move the cursor either horizontally or vertically. In this experiment, the general purpose BCI system BCI2000 is used to control signal acquisition, signal processing, and feedback to the user [3].</span></p>10aBrain10aCalibration10aElectrodes10aElectroencephalography10aHumans10aUser-Computer Interface1 aWilson, Adam, J1 aSchalk, Gerwin1 aWalton, Léo M1 aWilliams, Justin, C  uhttp://www.ncbi.nlm.nih.gov/pubmed/1964147905137nas a2200373   4500008004100000022001400041245007500055210006900130260001200199300001000211490000800221520406800229653001004297653001504307653001004322653001804332653002404350653002704374653001104401653000904412653002404421653002404445653001904469653003604488653004104524653002404565653002804589100001904617700001904636700002404655700001504679700002104694856004804715       2008                        eng d  a0165-027000aBrain-computer interfaces (BCIs): Detection Instead of Classification.0 aBraincomputer interfaces BCIs Detection Instead of Classificatio  c01/2008  a51-620 v1673 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Many studies over the past two decades have shown that people can use&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals to convey their intent to a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;through&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs). These devices operate by recording signals from the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;applications is the current requirement for preliminary analyses to identify the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;activity so that any change in&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">relevant</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.</span></p>10aAdult10aAlgorithms10aBrain10aBrain Mapping10aElectrocardiography10aElectroencephalography10aHumans10aMale10aMan-Machine Systems10aNormal Distribution10aOnline Systems10aSignal Detection, Psychological10aSignal Processing, Computer-Assisted10aSoftware Validation10aUser-Computer Interface1 aSchalk, Gerwin1 aBrunner, Peter1 aGerhardt, Lester, A1 aBischof, H1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1792013402262nas a2200193   4500008004100000022001400041245003000055210002800085260001200113300001100125490000600136520179600142653001001938653001401948653001101962653002801973100001902001856004802020       2008                        eng d  a1741-256000aBrain-computer symbiosis.0 aBraincomputer symbiosis  c03/2008  aP1-P150 v53 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The theoretical groundwork of the 1930s and 1940s and the technical advance of computers in the following decades provided the basis for dramatic increases in human efficiency. While computers continue to evolve, and we can still expect increasing benefits from their use, the interface between humans and computers has begun to present a serious impediment to full realization of the potential payoff. This paper is about the theoretical and practical possibility that direct communication between the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;can be used to overcome this impediment by improving or augmenting conventional forms of human communication. It is about the opportunity that the limitations of our body's input and output capacities can be overcome using direct interaction with the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, and it discusses the assumptions, possible limitations and implications of a technology that I anticipate will be a major source of pervasive changes in the coming decades.</span></p>10aBrain10aComputers10aHumans10aUser-Computer Interface1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/1831080405892nas a2200361   4500008004100000022001400041245010100055210006900156260001200225300001000237490000800247520480000255653001505055653002805070653001805098653002005116653002705136653002405163653001105187653000905198653001105207653003105218653003205249653002805281653004105309653002205350653002805372100002305400700002005423700002005443700001905463856004805482       2008                        eng d  a0165-027000aExtraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.0 aExtraction and localization of mesoscopic motor control signals   c01/2008  a63-810 v1673 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocorticogram (</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) recordings for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">neuroprosthetics</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;provide a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">mesoscopic</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;neural interfaces require appropriate feature&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">extraction</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and signal processing methods to identify and model in real-time signatures of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz)&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;voltages to determine if they provide viable&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;inputs for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;above the level of noisy background fluctuations in order to derive the commands for complex, continuous&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;tasks using amplitude modulated&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">.</span></p>10aAdolescent10aBiofeedback, Psychology10aBrain Mapping10aCerebral Cortex10aElectroencephalography10aEpilepsies, Partial10aFemale10aHand10aHumans10aMagnetic Resonance Imaging10aPhysical Therapy Modalities10aPsychomotor Performance10aSignal Processing, Computer-Assisted10aSpectrum Analysis10aUser-Computer Interface1 aSanchez, Justin, C1 aGunduz, Aysegul1 aCarney, Paul, R1 aPrincipe, Jose  uhttp://www.ncbi.nlm.nih.gov/pubmed/1758250704251nas a2200505   4500008004100000022001400041245009900055210006900154260001200223300001200235490000700247520283600254653003103090653001503121653001003136653001003146653001003156653002703166653002903193653001103222653001103233653001303244653000903257653001603266653001703282653003303299653001903332653002803351653001303379653002203392653001303414653004303427653002803470653001303498100001603511700002203527700001803549700002103567700001903588700002103607700002203628700002703650700002003677856004803697       2008                        eng d  a0361-923000aNon-invasive brain-computer interface system: towards its application as assistive technology.0 aNoninvasive braincomputer interface system towards its applicati  c04/2008  a796-8030 v753 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs) are systems that can translate&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;demonstrations to a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;useful BCI.</span></p>10aActivities of Daily Living10aAdolescent10aAdult10aBrain10aChild10aElectroencephalography10aEvoked Potentials, Motor10aFemale10aHumans10aLearning10aMale10aMiddle Aged10aMotor Skills10aMuscular Dystrophy, Duchenne10aPilot Projects10aProstheses and Implants10aRobotics10aSelf-Help Devices10aSoftware10aSpinal Muscular Atrophies of Childhood10aUser-Computer Interface10aVolition1 aCincotti, F1 aMattia, Donatella1 aAloise, Fabio1 aBufalari, Simona1 aSchalk, Gerwin1 aOriolo, Giuseppe1 aCherubini, Andrea1 aMarciani, Maria Grazia1 aBabiloni, Fabio  uhttp://www.ncbi.nlm.nih.gov/pubmed/1839452603145nas a2200373   4500008004100000022001400041245005700055210005400112260001200166300001000178490000700188520212100195653001002316653001502326653001802341653002102359653003302380653002702413653001302440653002202453653001102475653001102486653000902497653003502506653003102541653003202572100001902604700001902623700001902642700001702661700002402678700002102702856004802723       2008                        eng d  a1095-957200aReal-time detection of event-related brain activity.0 aRealtime detection of eventrelated brain activity  c11/2008  a245-90 v433 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The complexity and inter-individual variation of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.</span></p>10aAdult10aAlgorithms10aBrain Mapping10aComputer Systems10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aEvoked Potentials10aFemale10aHumans10aMale10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity1 aSchalk, Gerwin1 aLeuthardt, E C1 aBrunner, Peter1 aOjemann, J G1 aGerhardt, Lester, A1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1871854402352nas a2200445   4500008004100000245007200041210006900113260003300182520109300215653001501308653001001323653001501333653003401348653002001382653001801402653002401420653001501444653002701459653002801486653001301514653001101527653001401538653001401552653001101566653000901577653001601586653001701602653002201619653002401641653003401665653001101699653002801710100001901738700002101757700001901778700001701797700002001814700002401834856004801858       2008                        eng d00aThree cases of feature correlation in an electrocorticographic BCI.0 aThree cases of feature correlation in an electrocorticographic B  aVancouver, BCbIEEEc08/20083 aThree human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals.10aAdolescent10aAdult10aAlgorithms10aautomated pattern recognition10acontrol systems10adecorrelation10aElectrocardiography10aElectrodes10aElectroencephalography10aevoked motor potentials10aFeedback10aFemale10afrequency10ahospitals10aHumans10aMale10aMiddle Aged10aMotor Cortex10aSignal Processing10aStatistics as Topic10aTask Performance and Analysis10aTongue10aUser-Computer Interface1 aMiller, Kai, J1 aBlakely, Timothy1 aSchalk, Gerwin1 aNijs, Marcel1 aRao, Rajesh, PN1 aOjemann, Jeffrey, G  uhttp://www.ncbi.nlm.nih.gov/pubmed/1916391802177nas a2200361   4500008004100000022001400041245007200055210006900127260000900196300001200205520109300217653001501310653001001325653001501335653002401350653002901374653001101403653001101414653000901425653001601434653001701450653003501467653002401502653003401526653002801560100002001588700002101608700001901629700001701648700002101665700001701686856011201703       2008                        eng d  a1557-170X00aThree cases of feature correlation in an electrocorticographic BCI.0 aThree cases of feature correlation in an electrocorticographic B  c2008  a5318-213 aThree human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals.10aAdolescent10aAdult10aAlgorithms10aElectrocardiography10aEvoked Potentials, Motor10aFemale10aHumans10aMale10aMiddle Aged10aMotor Cortex10aPattern Recognition, Automated10aStatistics as Topic10aTask Performance and Analysis10aUser-Computer Interface1 aMiller, John, W1 aBlakely, Timothy1 aSchalk, Gerwin1 aNijs, Marcel1 aRao, Rajesh, P N1 aOjemann, J G  uhttps://www.neurotechcenter.org/publications/2008/three-cases-feature-correlation-electrocorticographic-bci04357nas a2200385   4500008004100000022001400041245009700055210006900152260001200221300001200233490000800245520323100253653001503484653001003499653001403509653001003523653001803533653004203551653002703593653003003620653001103650653001103661653000903672653003203681653002303713653002203736653002803758100002503786700002603811700001903837700002003856700002603876700002103902856004803923       2008                        eng d  a1388-245700aTowards an independent brain-computer interface using steady state visual evoked potentials.0 aTowards an independent braincomputer interface using steady stat  c02/2008  a399-4080 v1193 a<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSIONS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The&nbsp;<span class="highlight">data</span>&nbsp;demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">SIGNIFICANCE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.</p>10aAdolescent10aAdult10aAttention10aBrain10aBrain Mapping10aDose-Response Relationship, Radiation10aElectroencephalography10aEvoked Potentials, Visual10aFemale10aHumans10aMale10aPattern Recognition, Visual10aPhotic Stimulation10aSpectrum Analysis10aUser-Computer Interface1 aAllison, Brendan, Z.1 aMcFarland, Dennis, J.1 aSchalk, Gerwin1 aZheng, Shi Dong1 aMoore-Jackson, Melody1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1807720802662nas a2200409   4500008004100000022001400041245008400055210006900139260001200208300001000220490000600230520153900236653001501775653001001790653001801800653003701818653002001855653002401875653002601899653002701925653001301952653001101965653001101976653000901987653001301996653002802009100001902037700001602056700002602072700002002098700001602118700001702134700001302151700002102164700001902185856004802204       2008                        eng d  a1741-256000aTwo-dimensional movement control using electrocorticographic signals in humans.0 aTwodimensional movement control using electrocorticographic sign  c03/2008  a75-840 v53 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We show here that a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73% in a two-dimensional four-target center-out task in which chance accuracy was 25%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;practicality, and also indicate promising directions for further research.</span></p>10aAdolescent10aAdult10aBrain Mapping10aData Interpretation, Statistical10aDrug Resistance10aElectrocardiography10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aHumans10aMale10aMovement10aUser-Computer Interface1 aSchalk, Gerwin1 aMiller, K J1 aAnderson, Nicholas, R1 aWilson, Adam, J1 aSmyth, Matt1 aOjemann, J G1 aMoran, D1 aWolpaw, Jonathan1 aLeuthardt, E C  uhttp://www.ncbi.nlm.nih.gov/pubmed/1831081304121nas a2200457   4500008004100000022001400041245010800055210006900163260001200232300001100244490000700255520290000262653001503162653001003177653002103187653001203208653001803220653001003238653002403248653002703272653001103299653000903310653001103319653000903330653001603339653001703355653001303372653001203385653002203397653002803419653001103447653002803458653001303486100002303499700002603522700001903548700001603567700001303583700001903596856004803615       2008                        eng d  a1524-462800aUnique cortical physiology associated with ipsilateral hand movements and neuroprosthetic implications.0 aUnique cortical physiology associated with ipsilateral hand move  c12/2008  a3351-90 v393 a<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">BACKGROUND AND PURPOSE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;"><span class="highlight">Brain computer interfaces</span>&nbsp;(BCIs) offer little direct benefit to patients with hemispheric stroke because current platforms rely on signals derived from the contralateral motor cortex (the same region injured by the stroke). For BCIs to assist hemiparetic patients, the implant must use unaffected cortex ipsilateral to the affected limb. This requires the identification of distinct electrophysiological features from the motor cortex associated with ipsilateral hand movements.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">In this study we studied 6 patients undergoing temporary placement of intracranial electrode arrays. Electrocorticographic (ECoG) signals were recorded while the subjects engaged in specific ipsilateral or contralateral hand motor tasks. Spectral changes were identified with regards to frequency, location, and timing.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Ipsilateral hand movements were associated with electrophysiological changes that occur in lower frequency spectra, at distinct anatomic locations, and earlier than changes associated with contralateral hand movements. In a subset of 3 patients, features specific to ipsilateral and contralateral hand movements were used to control a cursor on a screen in real time. In ipsilateral derived control this was optimal with lower frequency spectra.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSIONS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">There are distinctive cortical electrophysiological features associated with ipsilateral movements which can be used for device control. These findings have implications for patients with hemispheric stroke because they offer a potential methodology for which a single hemisphere can be used to enhance the function of a stroke induced hemiparesis.</p>10aAdolescent10aAdult10aArtificial Limbs10aBionics10aBrain Mapping10aChild10aDominance, Cerebral10aElectroencephalography10aFemale10aHand10aHumans10aMale10aMiddle Aged10aMotor Cortex10aMovement10aParesis10aProsthesis Design10aPsychomotor Performance10aStroke10aUser-Computer Interface10aVolition1 aWisneski, Kimberly1 aAnderson, Nicholas, R1 aSchalk, Gerwin1 aSmyth, Matt1 aMoran, D1 aLeuthardt, E C  uhttp://www.ncbi.nlm.nih.gov/pubmed/1892745603153nas a2200481   4500008004100000022001400041245008200055210006900137260001200206300001000218490000700228520180600235653001002041653002802051653002002079653003602099653002402135653002702159653002402186653001102210653001102221653001602232653000902248653001602257653001902273653001702292653004102309653001302350653002502363653001702388653002802405653001202433100002002445700001802465700001302483700002502496700002402521700001802545700001802563700002102581700002102602856004802623       2008                        eng d  a1525-506900aVoluntary brain regulation and communication with electrocorticogram signals.0 aVoluntary brain regulation and communication with electrocortico  c08/2008  a300-60 v133 a<p><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.</span></p>10aAdult10aBiofeedback, Psychology10aCerebral Cortex10aCommunication Aids for Disabled10aDominance, Cerebral10aElectroencephalography10aEpilepsies, Partial10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aMotor Activity10aMotor Cortex10aSignal Processing, Computer-Assisted10aSoftware10aSomatosensory Cortex10aTheta Rhythm10aUser-Computer Interface10aWriting1 aHinterberger, T1 aWidman, Guido1 aLal, T N1 aHill, Jeremy, Jeremy1 aTangermann, Michael1 aRosenstiel, W1 aSchölkopf, B1 aElger, Christian1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/1849554102357nas a2200385   4500008004100000022001400041245009800055210006900153260001200222300001100234490000600245520131000251653001001561653001501571653000801586653001801594653002001612653002701632653002901659653001101688653001101699653000901710653001301719100001901732700001601751700002001767700002601787700001901813700001701832700001601849700001301865700002401878700002101902856004801923       2007                        eng d  a1741-256000aDecoding two-dimensional movement trajectories using electrocorticographic signals in humans.0 aDecoding twodimensional movement trajectories using electrocorti  c09/2007  a264-750 v43 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Signals from the brain could provide a non-muscular communication and control system, a brain-computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.</span></p>10aAdult10aAlgorithms10aArm10aBrain Mapping10aCerebral Cortex10aElectroencephalography10aEvoked Potentials, Motor10aFemale10aHumans10aMale10aMovement1 aSchalk, Gerwin1 aKubánek, J1 aMiller, John, W1 aAnderson, Nicholas, R1 aLeuthardt, E C1 aOjemann, J G1 aLimbrick, D1 aMoran, D1 aGerhardt, Lester, A1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1787342904391nas a2200409   4500008004100000022001400041245010700055210006900162260001200231300002900243490000700272520323000279653001003509653002203519653001803541653002503559653002603584653002703610653001103637653000903648653001103657653000903668653001603677653001703693653001703710653004103727653001103768100001903779700002003798700002603818700001903844700002003863700002003883700001303903700001703916856004803933       2007                        eng d  a1524-404000aElectrocorticographic Frequency Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex.0 aElectrocorticographic Frequency Alteration Mapping A Clinical Te  c04/2007  a260-70; discussion 270-10 v603 a<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocortical stimulation (ECS) has been well established for delineating the eloquent cortex. However, ECS is still coarse and inefficient in delineating regions of the functional cortex and can be hampered by after-discharges. Given these constraints, an adjunct approach to defining the motor cortex is the use of electrocorticographic signal changes associated with active regions of the cortex. The broad range of frequency oscillations are categorized into two main groups with respect to the sensorimotor cortex: low and high frequency bands. The low frequency bands tend to show a power reduction with cortical activation, whereas the high frequency bands show power increases. These power changes associated with the activated cortex could potentially provide a powerful tool in delineating areas of the motor cortex. We explore electrocorticographic signal alterations as they occur with activated regions of the motor cortex, as well as its potential in clinical brain mapping applications.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We evaluated seven patients who underwent invasive monitoring for seizure localization. Each patient had extraoperative ECS mapping to identify the motor cortex. All patients also performed overt hand and tongue motor tasks to identify associated frequency power changes in regard to location and degree of concordance with ECS results that localized either hand or tongue motor function.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The low frequency bands had a high sensitivity (88.9-100%) and a lower specificity (79.0-82.6%) for identifying electrodes with either hand or tongue ECS motor responses. The high frequency bands had a lower sensitivity (72.7-88.9%) and a higher specificity (92.4-94.9%) in correlation with the same respective ECS positive electrodes.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSION:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The concordance between stimulation and spectral power changes demonstrate the possible utility of electrocorticographic frequency alteration mapping as an adjunct method to improve the efficiency and resolution of identifying the motor cortex.</p>10aAdult10aBiological Clocks10aBrain Mapping10aElectric Stimulation10aElectrodes, Implanted10aElectroencephalography10aFemale10aHand10aHumans10aMale10aMiddle Aged10aMotor Cortex10aOscillometry10aSignal Processing, Computer-Assisted10aTongue1 aLeuthardt, E C1 aMiller, John, W1 aAnderson, Nicholas, R1 aSchalk, Gerwin1 aDowling, Joshua1 aMiller, John, W1 aMoran, D1 aOjemann, J G  uhttp://www.ncbi.nlm.nih.gov/pubmed/1741516203540nas a2200457   4500008004100000022001400041245004900055210004100104260001200145300001100157490000700168520234400175653001002519653001502529653001402544653001002558653002702568653002702595653002102622653001302643653001102656653000902667653000902676653001902685653001102704653003102715653002702746653000902773653001302782653003302795653004102828653002802869100002302897700001902920700002102939700002002960700001802980700002102998700001503019856004803034       2007                        eng d  a1053-811900aAn MEG-based brain-computer interface (BCI).0 aMEGbased braincomputer interface BCI  c07/2007  a581-930 v363 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">electroencephalography</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">(</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.</span></p>10aAdult10aAlgorithms10aArtifacts10aBrain10aElectroencephalography10aElectromagnetic Fields10aElectromyography10aFeedback10aFemale10aFoot10aHand10aHead Movements10aHumans10aMagnetic Resonance Imaging10aMagnetoencephalography10aMale10aMovement10aPrincipal Component Analysis10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aMellinger, Jürgen1 aSchalk, Gerwin1 aBraun, Christoph1 aPreissl, Hubert1 aRosenstiel, W1 aBirbaumer, Niels1 aKübler, A  uhttp://www.ncbi.nlm.nih.gov/pubmed/1747551101751nas a2200229   4500008004100000022001400041245012300055210006900178260001200247300001100259490000900270520101400279653001501293653002701308653001101335653003001346653001301376653004101389100001701430700002601447856004801473       2007                        eng d  a1557-170X00aNarrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG.0 aNarrowband vs broadband phase synchronization analysis applied t  c08/2007  a3864-70 v20073 aThis paper presents a comparison of the use of broadband and narrow band signals for phase synchronization analysis as applied to Independent Components of ictal and interictal scalp EEG in the context of seizure onset detection and prediction. Narrow band analysis for phase synchronization is found to be better performed in the present context than the broad band signal analysis. It has been observed that the phase synchronization of Independent Components in a narrow band (particularly the Gamma band) shows a prominent trend of increasing and decreasing synchronization at seizure onset near the epileptogenic area (spatially). This information is not always found to be consistent in analysis with the raw EEG signals, which may show spurious synchronization happening due to volume conduction effects. These observations lead us to believe that tracking changes in phase synchronization of narrow band activity, on continuous data records will be of great value in the context of seizure prediction.10aAlgorithms10aElectroencephalography10aHumans10aPredictive Value of Tests10aSeizures10aSignal Processing, Computer-Assisted1 aGupta, Disha1 aJames, Christopher, J  uhttp://www.ncbi.nlm.nih.gov/pubmed/1800284201913nas a2200349   4500008004100000022001400041245007900055210006900134260001200203300001100215520081300226653001001039653003601049653002101085653001101106653003101117653002001148653002201168653001301190653002801203100001601231700001801247700002101265700001901286700002101305700002201326700002101348700002001369700002701389700002201416856012501438       2007                        eng d  a1557-170X00aNon-invasive brain-computer interface system to operate assistive devices.0 aNoninvasive braincomputer interface system to operate assistive   c04/2007  a2532-53 aIn this pilot study, a system that allows disabled persons to improve or recover their mobility and communication within the surrounding environment was implemented and validated. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Fourteen patients with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program. All users utilized regular assistive control options (e.g., microswitches or head trackers) while four patients learned to operate the system by means of a non-invasive EEG-based Brain-Computer Interface, based on the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp.10aBrain10aCommunication Aids for Disabled10aComputer Systems10aHumans10aNeurodegenerative Diseases10aQuality of Life10aSelf-Help Devices10aSoftware10aUser-Computer Interface1 aCincotti, F1 aAloise, Fabio1 aBufalari, Simona1 aSchalk, Gerwin1 aOriolo, Giuseppe1 aCherubini, Andrea1 aDavide, Fabrizio1 aBabiloni, Fabio1 aMarciani, Maria Grazia1 aMattia, Donatella  uhttps://www.neurotechcenter.org/publications/2007/non-invasive-brain-computer-interface-system-operate-assistive-devices02201nas a2200289   4500008004100000022001400041245012000055210006900175260000900244300001100253490001200264520126500276653001501541653002801556653003301584653002701617653001301644653001101657653003501668653003301703653003101736653003201767100002601799700002101825700001701846856004801863       2007                        eng d  a1557-170X00aSpace-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity.0 aSpacetime ICA versus Ensemble ICA for ictal EEG analysis with co  c2007  a5473-60 v08/20073 aIn this proof-of-principle study we analyzed intracranial electroencephalogram recordings in patients with intractable focal epilepsy. We contrast two implementations of Independent Component Analysis (ICA) - Ensemble (or spatial) ICA (E-ICA) and Space-Time ICA (ST-ICA) in separating out the ictal components underlying the measurements. In each case we assess the outputs of the ICA algorithms by means of a non-linear method known as the Lempel-Ziv (LZ) complexity. LZ complexity quantifies the complexity of a time series and is well suited to the analysis of non-stationary biomedical signals of short length. Our results show that for small numbers of intracranial recordings, standard E-ICA results in marginal improvements in the separation as measured by the LZ complexity changes. ST-ICA using just 2 recording channels both near and far from the epileptic focus result in more distinct ictal components--although at this stage there is a subjective element to the separation process for ST-ICA. Our results are promising showing that it is possible to extract meaningful information from just 2 recording electrodes through ST-ICA, even if they are not directly over the seizure focus. This work is being further expanded for seizure onset analysis.10aAlgorithms10aArtificial Intelligence10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aHumans10aPattern Recognition, Automated10aPrincipal Component Analysis10aReproducibility of Results10aSensitivity and Specificity1 aJames, Christopher, J1 aAbásolo, Daniel1 aGupta, Disha  uhttp://www.ncbi.nlm.nih.gov/pubmed/1800325002238nas a2200325   4500008004100000022001400041245007500055210006900130260001200199300001200211490000700223520137400230653001001604653001801614653001101632653001101643653000901654653001601663653001701679653001301696100002001709700001901729700001901748700002101767700002601788700001301814700002001827700001701847856004801864       2007                        eng d  a1529-240100aSpectral Changes in Cortical Surface Potentials During Motor Movement.0 aSpectral Changes in Cortical Surface Potentials During Motor Mov  c02/2007  a2424-320 v273 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">In the first large study of its kind, we quantified changes in electrocorticographic signals associated with motor movement across 22 subjects with subdural electrode arrays placed for identification of seizure foci. Patients underwent a 5-7 d monitoring period with array placement, before seizure focus resection, and during this time they participated in the study. An interval-based motor-repetition task produced consistent and quantifiable spectral shifts that were mapped on a Talairach-standardized template cortex. Maps were created independently for a high-frequency band (HFB) (76-100 Hz) and a low-frequency band (LFB) (8-32 Hz) for several different movement modalities in each subject. The power in relevant electrodes consistently decreased in the LFB with movement, whereas the power in the HFB consistently increased. In addition, the HFB changes were more focal than the LFB changes. Sites of power changes corresponded to stereotactic locations in sensorimotor cortex and to the results of individual clinical electrical cortical mapping. Sensorimotor representation was found to be somatotopic, localized in stereotactic space to rolandic cortex, and typically followed the classic homunculus with limited extrarolandic representation.</span></p>10aAdult10aBrain Mapping10aFemale10aHumans10aMale10aMiddle Aged10aMotor Cortex10aMovement1 aMiller, John, W1 aLeuthardt, E C1 aSchalk, Gerwin1 aRao, Rajesh, P N1 aAnderson, Nicholas, R1 aMoran, D1 aMiller, John, W1 aOjemann, J G  uhttp://www.ncbi.nlm.nih.gov/pubmed/1732944102623nas a2200289   4500008004100000022001400041245008600055210007000141260001200211300001100223490000700234520175100241653001501992653002002007653002902027653002702056653002202083653001102105653001602116653003502132653002802167100002402195700001902219700002602238700002102264856004802285       2007                        eng d  a0018-929400aA µ-rhythm Matched Filter for  Continuous Control of a Brain-Computer Interface.0 aµrhythm Matched Filter for Continuous Control of a BrainComputer  c02/2007  a273-800 v543 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">continuous</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">amplitude fluctuations fail to capture essential amplitude/phase relationships of the mu and beta rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic mu rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic mu rhythm is proposed and its effectiveness as a matched filter is examined online for a one-dimensional cursor control task. The results suggest that amplitude/phase coupling exists between the mu and beta bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance.</span></p>10aAlgorithms10aCerebral Cortex10aCortical Synchronization10aElectroencephalography10aEvoked Potentials10aHumans10aImagination10aPattern Recognition, Automated10aUser-Computer Interface1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1727858403802nas a2200385   4500008004100000022001400041245008700055210006900142260001200211300001000223490000700233520264300240653001502883653001002898653003602908653002302944653002702967653002202994653001103016653002703027653002403054653003803078653002803116100002403144700002603168700002403194700001903218700002103237700002003258700002403278700002203302700002303324700002103347856004803368       2006                        eng d  a1534-432000aThe BCI competition III: Validating alternative approaches to actual BCI problems.0 aBCI competition III Validating alternative approaches to actual   c06/2006  a153-90 v143 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">A&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCI) is a system that allows its users to control external devices with&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, which produces&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">online</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.</span></p>10aAlgorithms10aBrain10aCommunication Aids for Disabled10aDatabases, Factual10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aSoftware Validation10aTechnology Assessment, Biomedical10aUser-Computer Interface1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aPfurtscheller, Gert1 aMillán, José, R1 aSchröder, Michael1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679228201675nas a2200397   4500008004100000022001400041245007000055210006600125260001200191300001100203490000700214520056900221653001500790653001800805653001000823653003600833653001400869653002700883653002100910653001100931653002100942653002400963653002700987653001301014653002801027100001601055700001501071700001601086700001301102700002301115700002201138700002301160700002701183700001901210856004801229       2006                        eng d  a1534-432000aBCI meeting 2005 - Workshop on Technology: Hardware and Software.0 aBCI meeting 2005 Workshop on Technology Hardware and Software  c06/2006  a128-310 v143 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop to review and evaluate the current state of BCI-related hardware and software. Technical requirements and current technologies, standardization procedures and future trends are covered. The main conclusion was recognition of the need to focus technical requirements on the users' needs and the need for consistent standards in BCI research.</span></p>10aAlgorithms10aBiotechnology10aBrain10aCommunication Aids for Disabled10aComputers10aElectroencephalography10aEquipment Design10aHumans10aInternationality10aMan-Machine Systems10aNeuromuscular Diseases10aSoftware10aUser-Computer Interface1 aCincotti, F1 aBianchi, L1 aBirch, Gary1 aGuger, C1 aMellinger, Jürgen1 aScherer, Reinhold1 aSchmidt, Robert, N1 aYáñez Suárez, Oscar1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679227602785nas a2200445   4500008004100000022001400041245015300055210006900208260001200277300001000289490000700299520147600306653001501782653002801797653002101825653002701846653002701873653002201900653001101922653001101933653001601944653000901960653001601969653001401985653003501999653002802034100002502062700001302087700002302100700002002123700002102143700001502164700001902179700001802198700002102216700001802237700001502255700002102270856004802291       2006                        eng d  a1534-432000aClassifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects.0 aClassifying EEG and ECoG signals without subject training for fa  c06/2006  a183-60 v143 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We summarize results from a series of related studies that aim to develop a motor-imagery-</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">based</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.</span></p>10aAlgorithms10aArtificial Intelligence10aCluster Analysis10aComputer User Training10aElectroencephalography10aEvoked Potentials10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aParalysis10aPattern Recognition, Automated10aUser-Computer Interface1 aHill, Jeremy, Jeremy1 aLal, T N1 aSchröder, Michael1 aHinterberger, T1 aWilhelm, Barbara1 aNijboer, F1 aMochty, Ursula1 aWidman, Guido1 aElger, Christian1 aSchölkopf, B1 aKübler, A1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679228902202nas a2200373   4500008004100000022001400041245007800055210006900133260001200202300001100214490000700225520114300232653001001375653001801385653002001403653003601423653002501459653002201484653001101506653001101517653001601528653000901544653002401553653002701577653002401604653002801628653001301656100002001669700002501689700002301714700001901737700002401756856004801780       2006                        eng d  a1534-432000aECoG factors underlying multimodal control of a brain-computer interface.0 aECoG factors underlying multimodal control of a braincomputer in  c06/2006  a246-500 v143 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Most current brain-computer interface (BCI) systems for humans use electroencephalographic activity recorded from the scalp, and may be limited in many ways. Electrocorticography (ECoG) is believed to be a minimally-invasive alternative to electroencephalogram (</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) for BCI systems, yielding superior signal characteristics that could allow rapid user training and faster communication rates. In addition, our preliminary results suggest that brain regions other than the sensorimotor cortex, such as auditory cortex, may be trained to control a BCI system using similar methods as those used to train motor regions of the brain. This could prove to be vital for users who have neurological disease, head trauma, or other conditions precluding the use of sensorimotor cortex for BCI control.</span></p>10aAdult10aBrain Mapping10aCerebral Cortex10aCommunication Aids for Disabled10aComputer Peripherals10aEvoked Potentials10aFemale10aHumans10aImagination10aMale10aMan-Machine Systems10aNeuromuscular Diseases10aSystems Integration10aUser-Computer Interface10aVolition1 aWilson, Adam, J1 aFelton, Elizabeth, A1 aGarell, Charles, P1 aSchalk, Gerwin1 aWilliams, Justin, C  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679230501844nas a2200289   4500008004100000022001400041245008100055210006900136260001200205300001000217490000700227520100900234653002001243653002701263653001301290653002201303653001101325653003101336653002801367653001501395100001901410700002001429700001901449700002101468700001701489856004801506       2006                        eng d  a1534-432000aElectrocorticography-based brain computer interface--the Seattle experience.0 aElectrocorticographybased brain computer interfacethe Seattle ex  c06/2006  a194-80 v143 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73%-100%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.</span></p>10aCerebral Cortex10aElectroencephalography10aEpilepsy10aEvoked Potentials10aHumans10aTherapy, Computer-Assisted10aUser-Computer Interface10aWashington1 aLeuthardt, E C1 aMiller, John, W1 aSchalk, Gerwin1 aRao, Rajesh, P N1 aOjemann, J G  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679229203287nas a2200265   4500008004100000022001400041245007900055210006900134260001200203300002600215490000700241520252600248653001002774653001102784653002402795653001302819653001702832653002802849653002802877100001902905700001902924700001302943700001702956856004802973       2006                        eng d  a1524-404000aThe emerging world of motor neuroprosthetics: a neurosurgical perspective.0 aemerging world of motor neuroprosthetics a neurosurgical perspec  c07/2006  a1-14; discussion 1-140 v593 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">A MOTOR NEUROPROSTHETIC device, or&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, is a machine that can take some type of signal from the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and convert that information into overt device control such that it reflects the intentions of the user's&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. In essence, these constructs can decode the electrophysiological signals representing motor intent. With the parallel evolution of neuroscience, engineering, and rapid computing, the era of clinical neuroprosthetics is approaching as a practical reality for people with severe motor impairment. Patients with such diseases as spinal cord injury, stroke, limb loss, and neuromuscular disorders may benefit through the implantation of these&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;that serve to augment their ability to communicate and interact with their environment. In the upcoming years, it will be important for the neurosurgeon to understand what a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;is, its fundamental principle of operation, and what the salient surgical issues are when considering implantation. We review the current state of the field of motor neuroprosthetics research, the early clinical applications, and the essential considerations from a neurosurgical perspective for the future.</span></p>10aBrain10aHumans10aMan-Machine Systems10aMovement10aNeurosurgery10aProstheses and Implants10aUser-Computer Interface1 aLeuthardt, E C1 aSchalk, Gerwin1 aMoran, D1 aOjemann, J G  uhttp://www.ncbi.nlm.nih.gov/pubmed/1682329404762nas a2200325   4500008004100000022001400041245009800055210006900153260001200222300000900234490000600243520379900249653001504048653002704063653002904090653001104119653001604130653001704146653001304163653003504176653003104211653003204242653002804274100001804302700002004320700001204340700001804352700001804370856004804388       2006                        eng d  a1741-256000aMulti-channel linear descriptors for event-related EEG collected in brain computer interface.0 aMultichannel linear descriptors for eventrelated EEG collected i  c03/2006  a52-80 v33 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">By three multi-channel linear descriptors, i.e. spatial&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">complexity</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(omega), field power (sigma) and frequency of field changes (phi),&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">event-related</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">data</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">event-related</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">data</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">event-related</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">event-related</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;patterns in the application of brain computer interfaces.</span></p>10aAlgorithms10aElectroencephalography10aEvoked Potentials, Motor10aHumans10aImagination10aMotor Cortex10aMovement10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aPei, Xiao-Mei1 aZheng, Shi Dong1 aXu, Jin1 aBin, Guang-yu1 aWang, Zuoguan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1651094201743nas a2200265   4500008004100000022001400041245005100055210004900106260001200155300001700167490000700184520102200191653001201213653002701225653001001252653001901262653002801281653001101309653001601320653004301336100002001379700001801399700001201417856004801429       2006                        eng d  a1671-710400aProgress of brain-neural function informatics.0 aProgress of brainneural function informatics  c11/2006  a399-406, 4620 v303 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Firstly the fundamental concept and research hotspots of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">-Neural Function Informatics (BNFI) are described. Then the main study fields and progresses of BNFI are expounded. Finally the prospects of BNFI research are given. Studies on BNFI not only promote the "</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;Science" progress, but also boost the industry of a new kind of medical instruments - function rehabilitation equipment and artificial functional prostheses.</span></p>10aAnimals10aBiomedical Engineering10aBrain10aBrain Diseases10aComputing Methodologies10aHumans10aInformatics10aNervous System Physiological Phenomena1 aZheng, Shi Dong1 aPei, Xiao-Mei1 aXu, Jin  uhttp://www.ncbi.nlm.nih.gov/pubmed/1730000302977nas a2200361   4500008004100000022001400041245007400055210006800129260001200197300001100209490000700220520195300227653001202180653001002192653002702202653002202229653001102251653002702262653001302289653001302302653001602315653003102331653001702362653002802379100002402407700002602431700001902457700002502476700002402501700002102525700002102546856004802567       2006                        eng d  a1534-432000aThe Wadsworth BCI Research and Development Program: At Home with BCI.0 aWadsworth BCI Research and Development Program At Home with BCI  c06/2006  a229-330 v143 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive,&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">electroencephalography</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">complexity</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.</span></p>10aAnimals10aBrain10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aNew York10aResearch10aSwitzerland10aTherapy, Computer-Assisted10aUniversities10aUser-Computer Interface1 aVaughan, Theresa, M1 aMcFarland, Dennis, J.1 aSchalk, Gerwin1 aSarnacki, William, A1 aKrusienski, Dean, J1 aSellers, Eric, W1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679230102711nas a2200289   4500008004100000022001400041245017800055210006900233260001200302300001000314490000700324520179900331653001502130653001002145653002302155653002602178653001102204653001802215653002502233653002302258100001802281700002002299700001802319700001802337700001802355856004802373       2005                        eng d  a0167-876000aDiscussion on "Towards a quantitative characterization of functional states of the brain: from the non-linear methodology to the global linear description" by J. Wackermann.0 aDiscussion on Towards a quantitative characterization of functio  c06/2005  a201-70 v563 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Wackermann (1999) [Wackermann, J., 1999. Towards a quantitative characterization of functional states of the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">: from the non-linear methodology to the global linear description. Int. J. Psychophysiol. 34, 65-80] proposed Sigma-phi-Omega system for describing the global&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;macro-state, in which Omega complexity was used to quantify the degree of synchrony between spatially distributed EEG processes. In this paper the effect of signal power on Omega complexity is discussed, which was not considered in Wackermann's paper (1999). Then an improved method for eliminating the effect of signal power on Omega complexity is proposed. Finally a case study on the degree of synchrony between two-channel EEG signals over different&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;regions during hand motor imagery is given. The results show that the improved Omega complexity measure would characterize the true degree of synchrony among the EEG signals by eliminating the influence of signal power.</span></p>10aAlgorithms10aBrain10aDiagnostic Imaging10aFunctional Laterality10aHumans10aLinear Models10aModels, Neurological10aNonlinear Dynamics1 aPei, Xiao-Mei1 aZheng, Shi Dong1 aZhang, Ai-hua1 aDuan, Fu-jian1 aBin, Guang-yu  uhttp://www.ncbi.nlm.nih.gov/pubmed/1586632402332nas a2200457   4500008004100000022001400041245009000055210006900145260001200214300001100226490000700237520103300244653000901277653003401286653002701320653002901347653003701376653001101413653001101424653001601435653000901451653001601460653001701476653001301493653001401506653002301520653002801543653002501571653002201596653002801618100001501646700001501661700002301676700002401699700001601723700001901739700002601758700002101784700002101805856004801826       2005                        eng d  a1526-632X00aPatients with ALS can use sensorimotor rhythms to operate a brain-computer interface.0 aPatients with ALS can use sensorimotor rhythms to operate a brai  c05/2005  a1775-70 v643 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">People with severe motor disabilities can maintain an acceptable quality of life if they can communicate.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">based</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;BCI could help maintain quality of life for people with ALS.</span></p>10aAged10aAmyotrophic Lateral Sclerosis10aElectroencephalography10aEvoked Potentials, Motor10aEvoked Potentials, Somatosensory10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aMotor Cortex10aMovement10aParalysis10aPhotic Stimulation10aProstheses and Implants10aSomatosensory Cortex10aTreatment Outcome10aUser-Computer Interface1 aKübler, A1 aNijboer, F1 aMellinger, Jürgen1 aVaughan, Theresa, M1 aPawelzik, H1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1591180902760nas a2200433   4500008004100000022001400041245011000055210006900165260001200234300001200246490000700258520140000265653001001665653001501675653003401690653002801724653001001752653001401762653002301776653002701799653002201826653001101848653003101859653003201890653002801922100002401950700002601974700001902000700002402019700001902043700002102062700002002083700002002103700002402123700002002147700002302167700002102190856011502211       2004                        eng d  a0018-929400aThe BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials.0 aBCI Competition 2003 Progress and perspectives in detection and   c06/2004  a1044-510 v513 aInterest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.10aAdult10aAlgorithms10aAmyotrophic Lateral Sclerosis10aArtificial Intelligence10aBrain10aCognition10aDatabases, Factual10aElectroencephalography10aEvoked Potentials10aHumans10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aCurio, Gabriel1 aVaughan, Theresa, M1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aNeuper, Christa1 aPfurtscheller, Gert1 aHinterberger, T1 aSchröder, Michael1 aBirbaumer, Niels  uhttps://www.neurotechcenter.org/publications/2004/bci-competition-2003-progress-and-perspectives-detection-and02705nas a2200337   4500008004100000022001400041245007000055210006400125260001200189300001200201490000700213520166200220653001501882653001001897653001401907653003601921653002501957653002701982653002102009653003102030653002202061653001102083653002402094653002802118100001902146700002602165700002002191700002102211700002102232856011402253       2004                        eng d  a0018-929400aBCI2000: a general-purpose brain-computer interface (BCI) system.0 aBCI2000 a generalpurpose braincomputer interface BCI system  c06/2004  a1034-430 v513 aMany laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.10aAlgorithms10aBrain10aCognition10aCommunication Aids for Disabled10aComputer Peripherals10aElectroencephalography10aEquipment Design10aEquipment Failure Analysis10aEvoked Potentials10aHumans10aSystems Integration10aUser-Computer Interface1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aHinterberger, T1 aBirbaumer, Niels1 aWolpaw, Jonathan  uhttps://www.neurotechcenter.org/publications/2004/bci2000-general-purpose-brain-computer-interface-bci-system03554nas a2200361   4500008004100000022001400041245007800055210006900133260001200202300001000214490000600224520253800230653001002768653001002778653003602788653002502824653003302849653002602882653002702908653002202935653001102957653001102968653001602979653000902995653002303004653002803027100001903055700001903074700002103093700001703114700001303131856004803144       2004                        eng d  a1741-256000aA brain-computer interface using electrocorticographic signals in humans.0 abraincomputer interface using electrocorticographic signals in h  c06/2004  a63-710 v13 a<p><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;can enable users to control a one-dimensional&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">.</span></p>10aAdult10aBrain10aCommunication Aids for Disabled10aComputer Peripherals10aDiagnosis, Computer-Assisted10aElectrodes, Implanted10aElectroencephalography10aEvoked Potentials10aFemale10aHumans10aImagination10aMale10aMovement Disorders10aUser-Computer Interface1 aLeuthardt, E C1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aOjemann, J G1 aMoran, D  uhttp://www.ncbi.nlm.nih.gov/pubmed/1587662400603nas a2200157   4500008004100000022001400041245016200055210006900217260001200286300003100298490000700329653001100336100002600347700002400373856004800397       2003                        eng d  a1050-054500aPotential problems in the differential diagnosis of (central) auditory processing disorder (CAPD or APD) and attention-deficit hyperactivity disorder (ADHD).0 aPotential problems in the differential diagnosis of central audi  c07/2003  a278–80; author reply 2800 v1410aHumans1 aMcFarland, Dennis, J.1 aCacace, Anthony, T.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1295631203064nas a2200373   4500008004100000022001400041245009000055210006900145260001200214300001000226490000700236520200500243653002902248653001002277653001502287653001402302653001002316653001802326653002702344653003002371653001302401653001102414653001602425653002802441653001302469653002002482653002802502653002202530100002102552700002602573700002402599700001902623856004802642       2003                        eng d  a1534-432000aThe Wadsworth Center brain-computer interface (BCI) research and development program.0 aWadsworth Center braincomputer interface BCI research and develo  c06/2003  a204-70 v113 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interface (BCI) research at the Wadsworth Center has focused primarily on using electroencephalogram (EEG) rhythms recorded from the scalp over sensorimotor cortex to&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;by improving the selection of signal features and their translation into device commands, by incorporating additional signal features, and by optimizing the adaptive interaction between the user and system. In addition, to facilitate the evaluation, comparison, and combination of alternative BCI methods, we have developed a general-purpose BCI system called BCI-2000 and have made it available to other research&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">groups</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. Finally, in collaboration with several other&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">groups</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.</span></p>10aAcademic Medical Centers10aAdult10aAlgorithms10aArtifacts10aBrain10aBrain Mapping10aElectroencephalography10aEvoked Potentials, Visual10aFeedback10aHumans10aMiddle Aged10aNervous System Diseases10aResearch10aResearch Design10aUser-Computer Interface10aVisual Perception1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/1289927506596nas a2200265   4500008004100000022001400041245006100055210005900116260001200175300001100187490000800198520581800206653001906024653003606043653002106079653002706100653001106127653002806138100002106166700002106187700002606208700002406234700002406258856004806282       2002                        eng d  a1388-245700aBrain-computer interfaces for communication and control.0 aBraincomputer interfaces for communication and control  c06/2002  a767-910 v1133 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10-25bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;option for those with motor disabilities and might also give those without disabilities a supplementary&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;channel or a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;channel useful in special circumstances.</span></p>10aBrain Diseases10aCommunication Aids for Disabled10aComputer Systems10aElectroencephalography10aHumans10aUser-Computer Interface1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aMcFarland, Dennis, J.1 aPfurtscheller, Gert1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/1204803803091nas a2200373   4500008004100000022001400041245008600055210006900141260001200210300001100222490000600233520197800239653001502217653002002232653003602252653002102288653002702309653002202336653001102358653002702369653004102396653002802437100002102465700002102486700001902507700002602526700001702552700001902569700002102588700001802609700001802627700002402645856004802669       2000                        eng d  a1063-652800aBrain-computer interface technology: a review of the first international meeting.0 aBraincomputer interface technology a review of the first interna  c06/2000  a164-730 v83 a<p>Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.</p>10aAlgorithms10aCerebral Cortex10aCommunication Aids for Disabled10aDisabled Persons10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aHeetderks, W J1 aMcFarland, Dennis, J.1 aPeckham, P H1 aSchalk, Gerwin1 aDonchin, Emanuel1 aQuatrano, L A1 aRobinson, C J1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/1089617800588nas a2200157   4500008004100000022001400041245017200055210006900227260001200296300001200308490000700320653001100327100002100338700002400359856004700383       1994                        eng d  a0013-469400aThe influence of stimulus intensity, contralateral masking and handedness on the temporal N1 and the T complex components of the auditory N1 wave, by John F. Connolly.0 ainfluence of stimulus intensity contralateral masking and handed  c07/1994  a71–760 v9110aHumans1 aWolpaw, Jonathan1 aCacace, Anthony, T.  uhttp://www.ncbi.nlm.nih.gov/pubmed/751784703423nas a2200337   4500008004100000022001400041245009300055210006900148260001200217300001200229490000700241520247800248653001202726653002402738653002602762653001002788653001602798653002802814653001102842653002702853653002902880653001002909653002602919100001202945700001702957700001702974700001902991700001403010700001403024856004703038       1994                        eng d  a0009-733000aWave-front curvature as a cause of slow conduction and block in isolated cardiac muscle.0 aWavefront curvature as a cause of slow conduction and block in i  c12/1994  a1014-280 v753 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We have investigated the role of wave-front curvature on propagation by following the wave front that was diffracted through a narrow isthmus created in a two-dimensional ionic model (Luo-Rudy) of ventricular muscle and in a thin (0.5-mm) sheet of sheep ventricular epicardial muscle. The electrical activity in the experimental preparations was imaged by using a high-resolution video camera that monitored the changes in fluorescence of the potentiometric dye di-4-ANEPPS on the surface of the tissue. Isthmuses were created both parallel and perpendicular to the fiber orientation. In both numerical and biological experiments, when a planar wave front reached the isthmus, it was diffracted to an elliptical wave front whose pronounced curvature was very similar to that of a wave front initiated by point stimulation. In addition, the velocity of propagation was reduced in relation to that of the original planar wave. Furthermore, as shown by the numerical results, wave-front curvature changed as a function of the distance from the isthmus. Such changes in local curvature were accompanied by corresponding changes in velocity of propagation. In the model, the critical isthmus width was 200 microns for longitudinal propagation and 600 microns for transverse propagation of a single planar wave initiated proximal to the isthmus. In the experiments, propagation depended on the width of the isthmus for a fixed stimulation frequency. Propagation through an isthmus of fixed width was rate dependent both along and across fibers. Thus, the critical isthmus width for propagation was estimated in both directions for different frequencies of stimulation. In the longitudinal direction, for cycle lengths between 200 and 500 milliseconds, the critical width was &lt; 1 mm; for 150 milliseconds, it was estimated to be between 1.3 and 2 mm; and for the maximum frequency of stimulation (117 +/- 15 milliseconds), it was &gt; 2.5 mm. In the transverse direction, critical width was between 1.78 and 2.32 mm for a basic cycle length of 200 milliseconds. It increased to values between 2.46 and 3.53 mm for a basic cycle length of 150 milliseconds. The overall results demonstrate that the curvature of the wave front plays an important role in propagation in two-dimensional cardiac muscle and that changes in curvature may cause slow conduction or block.</span></p>10aAnimals10aComputer Simulation10aElectric Conductivity10aHeart10aHeart Block10aHeart Conduction System10aHumans10aModels, Cardiovascular10aMotion Pictures as Topic10aSheep10aStaining and Labeling1 aCabo, C1 aPertsov, A V1 aBaxter, Bill1 aDavidenko, J M1 aGray, R A1 aJalife, J  uhttp://www.ncbi.nlm.nih.gov/pubmed/752510101136nas a2200193   4500008004100000022001400041245007700055210006900132260001200201300001400213490000700227520056400234653001100798100002000809700002100829700002400850700002100874856004700895       1983                        eng d  a0013-469400aLate auditory evoked potentials can occur without brain stem potentials.0 aLate auditory evoked potentials can occur without brain stem pot  c10/1983  a304–3080 v563 aThe sequence of early, middle and late auditory evoked potentials is well known. However, it is unknown whether the late (60-250 msec) potentials can occur independently of the early, brain stem potentials. Therefore, in 6 subjects with markedly abnormal or absent brain stem potentials, we recorded two of the late potentials: the vertex potential and the T-complex. The latter is a putative product of auditory cortex. Both of these potentials were clearly evident in all patients in spite of the absence of or marked abnormalities in brain stem potentials.10aHumans1 aSatya-Murti, S.1 aWolpaw, Jonathan1 aCacace, Anthony, T.1 aSchaffer, C., A.  uhttp://www.ncbi.nlm.nih.gov/pubmed/6193943