@article {4567, title = {IpsiHand Brain-Computer Interface Therapy Induces Broad Upper Extremity Motor Rehabilitation in Chronic Stroke.}, journal = {Neurorehabil Neural Repair}, volume = {39}, year = {2025}, month = {2025 Jan}, pages = {74-86}, abstract = {

BACKGROUND: 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.

OBJECTIVES: 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.

METHODS: 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.

RESULTS: 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.

CONCLUSIONS: 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.

TRIAL REGISTRATION: Advarra Study: https://classic.clinicaltrials.gov/ct2/show/NCT04338971 and Washington University Study: https://classic.clinicaltrials.gov/ct2/show/NCT03611855.

}, keywords = {Adult, Aged, brain-computer interfaces, Chronic Disease, Electroencephalography, Female, Gamma Rhythm, Humans, Male, Middle Aged, Motor Activity, Paresis, Prospective Studies, Recovery of Function, Stroke, Stroke Rehabilitation, Theta Rhythm, Upper Extremity}, issn = {1552-6844}, doi = {10.1177/15459683241287731}, author = {Rustamov, Nabi and Souders, Lauren and Sheehan, Lauren and Carter, Alexandre and Leuthardt, Eric C} } @article {4584, title = {Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods.}, journal = {J Neurosci Methods}, volume = {404}, year = {2024}, month = {2024 Apr}, pages = {110056}, abstract = {

BACKGROUND: 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.

NEW METHODS: We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes{\textquoteright} 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.

RESULTS: 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.

COMPARISON WITH EXISTING METHODS: 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.

CONCLUSION: 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.

}, keywords = {Brain, Cerebral Cortex, Electrodes, Electrodes, Implanted, Electroencephalography, Humans, Magnetic Resonance Imaging}, issn = {1872-678X}, doi = {10.1016/j.jneumeth.2024.110056}, author = {Blenkmann, Alejandro Omar and Leske, Sabine Liliana and Llorens, Ana{\"\i}s and Lin, Jack J and Chang, Edward F and Brunner, Peter and Schalk, Gerwin and Ivanovic, Jugoslav and Larsson, P{\r a}l Gunnar and Knight, Robert Thomas and Endestad, Tor and Solbakk, Anne-Kristin} } @article {4582, title = {Motor imagery ability scores are related to cortical activation during gait imagery.}, journal = {Sci Rep}, volume = {14}, year = {2024}, month = {2024 Mar 03}, pages = {5207}, abstract = {

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{\textquoteright}s and Spearman{\textquoteright}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.

}, keywords = {Adult, Animals, Brain, Cell Membrane, Electroencephalography, Gait, Gastropoda, Humans, Walking}, issn = {2045-2322}, doi = {10.1038/s41598-024-54966-1}, author = {Putzolu, Martina and Samogin, Jessica and Bonassi, Gaia and Cosentino, Carola and Mezzarobba, Susanna and Botta, Alessandro and Avanzino, Laura and Mantini, Dante and Vato, Alessandro and Pelosin, Elisa} } @article {4576, title = {Multisensory flicker modulates widespread brain networks and reduces interictal epileptiform discharges.}, journal = {Nat Commun}, volume = {15}, year = {2024}, month = {2024 Apr 11}, pages = {3156}, abstract = {

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.

}, keywords = {Brain, Cross-Over Studies, Electroencephalography, Epilepsies, Partial, Epilepsy, Humans, Temporal Lobe}, issn = {2041-1723}, doi = {10.1038/s41467-024-47263-y}, author = {Blanpain, Lou T and Cole, Eric R and Chen, Emily and Park, James K and Walelign, Michael Y and Gross, Robert E and Cabaniss, Brian T and Willie, Jon T and Singer, Annabelle C} } @article {4568, title = {Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.}, journal = {Elife}, volume = {12}, year = {2024}, month = {2024 Sep 06}, abstract = {

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{\textquoteright}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{\textquoteright}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.

}, keywords = {Brain, Electrocorticography, Electroencephalography, Humans, Signal Processing, Computer-Assisted}, issn = {2050-084X}, doi = {10.7554/eLife.91605}, author = {Cho, Hohyun and Adamek, Markus and Willie, Jon T and Brunner, Peter} } @article {4583, title = {A novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study.}, journal = {Sci Rep}, volume = {14}, year = {2024}, month = {2024 Feb 10}, pages = {3433}, abstract = {

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{\textquoteright}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 {\textpm} 0.25 MAD, p = 0.03, q = 0.05) and pain interference (1.79 {\textpm} 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.

}, keywords = {brain-computer interfaces, Chronic Pain, Electroencephalography, Humans, Longitudinal Studies, Neurofeedback, Non-Randomized Controlled Trials as Topic, Pilot Projects}, issn = {2045-2322}, doi = {10.1038/s41598-024-53261-3}, author = {Demarest, Phillip and Rustamov, Nabi and Swift, James and Xie, Tao and Adamek, Markus and Cho, Hohyun and Wilson, Elizabeth and Han, Zhuangyu and Belsten, Alexander and Luczak, Nicholas and Brunner, Peter and Haroutounian, Simon and Leuthardt, Eric C} } @article {4450, title = {Towards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations.}, journal = {J Neurol}, volume = {270}, year = {2023}, month = {03/2023}, pages = {1323-1336}, abstract = {

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.

}, keywords = {Amyotrophic Lateral Sclerosis, brain-computer interfaces, Electroencephalography, Humans, Self-Help Devices, Speech}, issn = {1432-1459}, doi = {10.1007/s00415-022-11464-6}, author = {Vansteensel, Mariska J and Klein, Eran and van Thiel, Ghislaine and Gaytant, Michael and Simmons, Zachary and Wolpaw, Jonathan R and Vaughan, Theresa M} } @article {4360, title = {Neural oscillations during motor imagery of complex gait: an HdEEG study.}, journal = {Sci Rep}, volume = {12}, year = {2022}, month = {03/2022}, pages = {4314}, abstract = {

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.

}, keywords = {Brain, Electroencephalography, Gait, Humans, Imagery, Psychotherapy, Imagination, Walking}, issn = {2045-2322}, doi = {10.1038/s41598-022-07511-x}, author = {Putzolu, Martina and Samogin, Jessica and Cosentino, Carola and Mezzarobba, Susanna and Bonassi, Gaia and Lagravinese, Giovanna and Vato, Alessandro and Mantini, Dante and Avanzino, Laura and Pelosin, Elisa} } @article {4495, title = {Brain-computer interface-based assessment of color vision.}, journal = {J Neural Eng}, volume = {18}, year = {2021}, month = {2021 Nov 26}, abstract = {

Present methods for assessing color vision require the person{\textquoteright}s active participation. Here we describe a brain-computer interface-based method for assessing color vision that does not require the person{\textquoteright}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.

}, keywords = {brain-computer interfaces, Color Vision, Electroencephalography, Evoked Potentials, Visual, Humans, Light, Photic Stimulation, Research Design}, issn = {1741-2552}, doi = {10.1088/1741-2552/ac3264}, author = {Norton, James J S and DiRisio, Grace F and Carp, Jonathan S and Norton, Amanda E and Kochan, Nicholas S and Wolpaw, Jonathan R} } @article {4362, title = {A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {29}, year = {2021}, month = {2021}, pages = {1766-1773}, abstract = {

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.

}, keywords = {Algorithms, brain-computer interfaces, Electroencephalography, Evoked Potentials, Visual, Humans, Photic Stimulation}, issn = {1558-0210}, doi = {10.1109/TNSRE.2021.3106876}, author = {Habibzadeh, Hadi and Norton, James J S and Vaughan, Theresa M and Soyata, Tolga and Zois, Daphney-Stavroula} } @article {4298, title = {Brain-computer interfaces for people with amyotrophic lateral sclerosis}, journal = {Handbook of Clinical Neurology}, volume = {168}, year = {2020}, month = {03/2020}, chapter = {33}, abstract = {A brain-computer interface (BCI) records and extracts features from brain signals, and translates these features into commands that can replace, restore, enhance, supplement, or improve natural CNS outputs. As demonstrated in the other chapters of this book, the focus of the work of the last three decades of BCI research has been the replacement, restoration, or improvement of diminished or lost function in people with CNS disease or injury including those with amyotrophic lateral sclerosis (ALS). Due in part to the desire to conduct controlled studies, and, in part, to the complexity of BCI technology, most of this work has been carried out in laboratories with healthy controls or with limited numbers of potential consumers with a variety of diagnoses under supervised conditions. The intention of this chapter is to describe the growing body of BCI research that has included people with amyotrophic lateral sclerosis (ALS). People in the late stages of ALS can lose all voluntary control, including the ability to communicate; and while recent research has provided new insights into underlying mechanisms, ALS remains a disease with no cure. As a result, people with ALS and their families, caregivers, and advocates have an active interest in both the current and potential capabilities of BCI technology. The focus of BCI research for people with ALS is on communication, and this topic is well covered elsewhere in this volume. This chapter focuses on the efforts dedicated to make BCI technology useful to people with ALS in their daily lives with a discussion of how researchers, clinicians, and patients must become partners in that process.}, keywords = {Amyotrophic Lateral Sclerosis, Augmentative and alternative communication, Electroencephalography}, doi = {10.1016/B978-0-444-63934-9.00004-4}, url = {https://www.sciencedirect.com/science/article/pii/B9780444639349000044}, author = {T. Vaughan} } @article {4367, title = {Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {28}, year = {2020}, month = {05/2020}, pages = {1198-1207}, abstract = {

OBJECTIVE: 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.

METHODS: 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).

RESULTS: Over all the subjects, we obtained an average accuracy of 81.3\%{\textpm}5.7\% within comparatively short times (< 4 sec) in the fNIRS-VM protocol relative to an average accuracy of 74.0\%{\textpm}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\%{\textpm}2.0\%) over the P3S (61.8\%{\textpm}1.5\%).

SIGNIFICANCE: Our findings indicate the potential efficacy of our proposed system for communication and control for late-stage ALS patients.

}, keywords = {Amyotrophic Lateral Sclerosis, brain-computer interfaces, Communication, Electroencephalography, Humans, Spectroscopy, Near-Infrared}, issn = {1558-0210}, doi = {10.1109/TNSRE.2020.2980772}, author = {Borgheai, Seyyed Bahram and McLinden, John and Zisk, Alyssa Hillary and Hosni, Sarah Ismail and Deligani, Roohollah Jafari and Abtahi, Mohammadreza and Mankodiya, Kunal and Shahriari, Yalda} } @article {4334, title = {iEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes.}, journal = {J Neural Eng}, volume = {17}, year = {2019}, month = {12/2019}, pages = {016016}, abstract = {

OBJECTIVE: 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.

APPROACH: 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.

MAIN RESULTS: 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.

SIGNIFICANCE: 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.

}, keywords = {Brain, Brain Mapping, Electrocorticography, Electrodes, Implanted, Electroencephalography, Humans, Magnetic Resonance Imaging}, issn = {1741-2552}, doi = {10.1088/1741-2552/ab51a5}, author = {Li, Guangye and Jiang, Shize and Chen, Chen and Peter Brunner and Wu, Zehan and Schalk, Gerwin and Chen, Liang and Zhang, Dingguo} } @article {3415, title = {Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users.}, journal = {Arch Phys Med Rehabil}, volume = {96}, year = {2015}, month = {03/2015}, pages = {S27-32}, abstract = {

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.

}, keywords = {Algorithms, brain-computer interfaces, Cognition, Disabled Persons, Electroencephalography, Environment, Humans, Patient Selection, Physical Therapy Modalities}, issn = {1532-821X}, doi = {10.1016/j.apmr.2014.03.036}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25721544}, author = {K{\"u}bler, Andrea and Holz, Elisa Mira and Sellers, Eric W and Theresa M Vaughan} } @article {3395, title = {Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {22}, year = {2014}, month = {07/2014}, pages = {847-57}, abstract = {Movement 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.}, keywords = {Algorithms, Artificial Intelligence, brain-computer interfaces, Data Interpretation, Statistical, Electroencephalography, Evoked Potentials, Motor, Humans, Imagination, Motor Cortex, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Spatio-Temporal Analysis}, issn = {1558-0210}, doi = {10.1109/TNSRE.2014.2315717}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24723632}, author = {Lu, Jun and Xie, Kan and Dennis J. McFarland} } @article {3370, title = {Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.}, journal = {Amyotroph Lateral Scler Frontotemporal Degener}, volume = {15}, year = {2014}, month = {06/2014}, pages = {207-15}, abstract = {Brain-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 {\texttimes} 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 ({\textpm} 3)\% (range 71-100\%), which is adequate for communication (G70 group). Eight averaged 12 ({\textpm} 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.}, keywords = {Adult, Aged, Amyotrophic Lateral Sclerosis, Biofeedback, Psychology, brain-computer interfaces, Communication Disorders, Electroencephalography, Event-Related Potentials, P300, Female, Humans, Male, Middle Aged, Online Systems, Photic Stimulation, Psychomotor Performance, Reaction Time}, issn = {2167-9223}, doi = {10.3109/21678421.2013.865750}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24555843}, author = {McCane, Lynn M and Sellers, Eric W and Dennis J. McFarland and Mak, Joseph N and Carmack, C Steve and Zeitlin, Debra and Jonathan Wolpaw and Theresa M Vaughan} } @article {3386, title = {A practical, intuitive brain-computer interface for communicating {\textquoteright}yes{\textquoteright} or {\textquoteright}no{\textquoteright} by listening.}, journal = {J Neural Eng}, volume = {11}, year = {2014}, month = {06/2014}, pages = {035003}, abstract = {OBJECTIVE: 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 {\textquoteright}yes{\textquoteright} and {\textquoteright}no{\textquoteright}) 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{\textquoteright} performance with word stimuli (mean 77\% {\textpm} 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73\% {\textpm} 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.}, keywords = {Adult, Aged, Algorithms, Auditory Perception, brain-computer interfaces, Communication Aids for Disabled, Electroencephalography, Equipment Design, Equipment Failure Analysis, Female, Humans, Male, Man-Machine Systems, Middle Aged, Quadriplegia, Treatment Outcome, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/11/3/035003}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24838278}, author = {Jeremy Jeremy Hill and Ricci, Erin and Haider, Sameah and McCane, Lynn M and Susan M Heckman and Jonathan Wolpaw and Theresa M Vaughan} } @article {3407, title = {Real-time functional mapping: potential tool for improving language outcome in pediatric epilepsy surgery.}, journal = {J Neurosurg Pediatr}, volume = {14}, year = {2014}, month = {09/2014}, pages = {287-95}, abstract = {

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.

}, keywords = {Adolescent, Anticonvulsants, Brain Mapping, Cerebral Cortex, Electric Stimulation, Electroencephalography, Epilepsies, Partial, Female, Humans, Language, Neuropsychological Tests, Sensitivity and Specificity, Speech}, issn = {1933-0715}, doi = {10.3171/2014.6.PEDS13477}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24995815}, author = {Korostenskaja, Milena and Chen, Po-Ching and Salinas, Christine M and Westerveld, Michael and Peter Brunner and Gerwin Schalk and Cook, Jane C and Baumgartner, James and Lee, Ki H} } @article {3377, title = {Novel inter-hemispheric white matter connectivity in the BTBR mouse model of autism.}, journal = {Brain Res}, volume = {1513}, year = {2013}, month = {06/2013}, pages = {26-33}, abstract = {Alterations in the volume, density, connectivity and functional activation of white matter tracts are reported in some individuals with autism and may contribute to their abnormal behaviors. The BTBR (BTBR T+tf/J) inbred strain of mouse, is used to model facets of autism because they develop low social behaviors, stereotypical and immune changes similar to those found in people with autism. Previously, it was thought a total absence of corpus callosal interhemispheric connective tissues in the BTBR mice may underlie their abnormal behaviors. However, postnatal lesions of the corpus callosum do not precipitate social behavioral problems in other strains of mice suggesting a flaw in this theory. In this study we used digital pathological methods to compare subcortical white matter connective tracts in the BTBR strain of mice with those found in the C57Bl/6 mouse and those reported in a standardized mouse brain atlas. We report, for the first time, a novel connective subcortical interhemispheric bridge of tissue in the posterior, but not anterior, cerebrum of the BTBR mouse. These novel connective tissues are comprised of myelinated fibers, with reduced myelin basic protein levels (MBP) compared to levels in the C57Bl/6 mouse. We used electrophysiological analysis and found increased inter-hemispheric connectivity in the posterior hemispheres of the BTBR strain compared with the anterior hemispheres. The conduction velocity was slower than that reported in normal mice. This study shows there is novel abnormal interhemispheric connectivity in the BTBR strain of mice, which may contribute to their behavioral abnormalities.}, keywords = {Analysis of Variance, Animals, Autistic Disorder, Brain, Corpus Callosum, Disease Models, Animal, Electroencephalography, Enzyme-Linked Immunosorbent Assay, Female, Functional Laterality, Image Processing, Computer-Assisted, Male, Mice, Mice, Inbred C57BL, Mice, Neurologic Mutants, Microtubule-Associated Proteins, Myelin Basic Protein, Nerve Fibers, Myelinated, Neuroimaging, Spectrum Analysis}, issn = {1872-6240}, doi = {10.1016/j.brainres.2013.04.001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23570707}, author = {Miller, V M and Disha Gupta and Neu, N and Cotroneo, A and Chadwick B. Boulay and Seegal, R F} } @article {3399, title = {Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface.}, journal = {Brain Res Bull}, volume = {87}, year = {2012}, month = {01/2012}, pages = {130-4}, abstract = {Measures 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.}, keywords = {Algorithms, Brain, Electroencephalography, Humans, Motor Cortex, User-Computer Interface}, issn = {1873-2747}, doi = {10.1016/j.brainresbull.2011.09.019}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21985984}, author = {Krusienski, Dean J and Dennis J. McFarland and Jonathan Wolpaw} } @article {2134, title = {Causal influence of gamma oscillations on the sensorimotor rhythm.}, journal = {Neuroimage}, volume = {56}, year = {2011}, month = {05/2011}, pages = {837-42}, abstract = {

Gamma oscillations of the electromagnetic field of the\ brain\ are known to be involved in a variety of cognitive processes, and are believed to be fundamental for information processing within the\ brain. While gamma oscillations have been shown to be correlated with\ brain\ rhythms at different frequencies, to date no empirical evidence has been presented that supports a causal influence of gamma oscillations on other\ brain\ 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\ brain, and is of particular importance for\ brain-computer interfaces\ (BCIs). As modulation of the SMR is typically used in BCIs to infer a subject{\textquoteright}s intention, our findings entail that gamma oscillations have a causal influence on a subject{\textquoteright}s capability to utilize a\ BCI\ for means of communication.

}, keywords = {Adult, Cerebral Cortex, Electroencephalography, Female, Humans, Imagination, Male, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2010.04.265}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20451626}, author = {Grosse-Wentrup, Moritz and Sch{\"o}lkopf, B and Jeremy Jeremy Hill} } @article {2203, title = {Current Trends in Hardware and Software for Brain-Computer Interfaces (BCIs).}, journal = {J Neural Eng}, volume = {8}, year = {2011}, month = {04/2011}, pages = {025001}, abstract = {

A\ brain-computer interface\ (BCI) provides a non-muscular communication channel to people with and without disabilities.\ BCI\ devices consist of hardware and software.\ BCI\ hardware records signals from the\ brain, either invasively or non-invasively, using a series of device components.\ BCI\ software then translates these signals into device output commands and provides feedback. One may categorize different types of\ BCI\ applications into the following four categories: basic\ research, clinical/translational\ research, consumer products, and emerging applications. These four categories use\ BCI\ hardware and software, but have different sets of requirements. For example, while basic\ research\ 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\ BCI\ applications. The results indicate that\ BCI\ technology is in transition from isolated demonstrations to systematic\ research\ and commercial\ development. This process requires several multidisciplinary efforts, including the\ development\ of better integrated and more robust\ BCI\ hardware and software, the definition of standardized\ interfaces, and the\ developmentof certification, dissemination and reimbursement procedures.

}, keywords = {Biofeedback, Psychology, Brain, Brain Mapping, Electroencephalography, Equipment Design, Equipment Failure Analysis, Humans, Man-Machine Systems, Software, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/8/2/025001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21436536}, author = {Peter Brunner and Bianchi, L and Guger, C and Cincotti, F and Gerwin Schalk} } @article {2151, title = {Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {8}, year = {2011}, month = {08/2011}, pages = {046028}, abstract = {

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 {\textquoteright}read the mind{\textquoteright}, 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.

}, keywords = {Adolescent, Adult, Brain, Brain Mapping, Cerebral Cortex, Communication Aids for Disabled, Data Interpretation, Statistical, Discrimination (Psychology), Electrodes, Implanted, Electroencephalography, Epilepsy, Female, Functional Laterality, Humans, Male, Middle Aged, Movement, Speech Perception, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/8/4/046028}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21750369}, author = {Pei, Xiao-Mei and Barbour, Dennis L and Leuthardt, E C and Gerwin Schalk} } @article {2133, title = {A graphical model framework for decoding in the visual ERP-based BCI speller.}, journal = {Neural Comput}, volume = {23}, year = {2011}, month = {01/2011}, pages = {160-82}, abstract = {

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\ brain\ signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between\ brain\ 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\ brain\ 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.

}, keywords = {Artificial Intelligence, Computer User Training, Discrimination Learning, Electroencephalography, Evoked Potentials, Evoked Potentials, Visual, Humans, Language, Models, Neurological, Models, Theoretical, Reading, Signal Processing, Computer-Assisted, User-Computer Interface, Visual Cortex, Visual Perception}, issn = {1530-888X}, doi = {10.1162/NECO_a_00066}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20964540}, author = {Martens, S M M and Mooij, J M and Jeremy Jeremy Hill and Farquhar, Jason and Sch{\"o}lkopf, B} } @article {2204, title = {Nonuniform high-gamma (60-500 Hz) power changes dissociate cognitive task and anatomy in human cortex.}, journal = {J Neurosci}, volume = {31}, year = {2011}, month = {02/2011}, pages = {2091-100}, abstract = {

High-gamma-band (\>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{\textquoteright}s area, and superior temporal gyrus), these behavior- and location-dependent power changes evidenced nonuniform\ trends\ 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.

}, keywords = {Acoustic Stimulation, Adolescent, Adult, Analysis of Variance, Brain Mapping, Brain Waves, Cerebral Cortex, Cognition Disorders, Electroencephalography, Epilepsy, Evoked Potentials, Female, Humans, Male, Middle Aged, Neuropsychological Tests, Nonlinear Dynamics, Photic Stimulation, Reaction Time, Spectrum Analysis, Time Factors, Vocabulary}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4722-10.2011}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21307246}, author = {Charles M Gaona and Sharma, Mohit and Zachary V. Freudenberg and Breshears, Jonathan and Bundy, David T and Roland, Jarod and Barbour, Dennis L and Gerwin Schalk and Leuthardt, E C} } @article {2208, title = {Proceedings of the Second International Workshop on Advances in Electrocorticography.}, journal = {Epilepsy Behav}, volume = {22}, year = {2011}, month = {12/2011}, pages = {641-50}, abstract = {

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.

}, keywords = {Brain, Brain Mapping, Brain Waves, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, United States, User-Computer Interface}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2011.09.028}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22036287}, author = {A L Ritaccio and Boatman-Reich, Dana and Peter Brunner and Cervenka, Mackenzie C and Cole, Andrew J and Nathan E. Crone and Duckrow, Robert and Korzeniewska, Anna and Litt, Brian and Miller, John W and Moran, D and Parvizi, Josef and Viventi, Jonathan and Williams, Justin C and Gerwin Schalk} } @article {2150, title = {Spatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition.}, journal = {Neuroimage}, volume = {54}, year = {2011}, month = {02/2011}, pages = {2960-72}, abstract = {

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\ brain\ 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{\textquoteright}s verbal response. Overt word production was primarily associated with HG changes in the superior and middle parts of temporal lobe, Wernicke{\textquoteright}s area, the supramarginal gyrus, Broca{\textquoteright}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{\textquoteright}s own voice resulted in HG power changes in superior temporal lobe and Wernicke{\textquoteright}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\ current\ understanding of word processing in humans.

}, keywords = {Adolescent, Adult, Brain, Brain Mapping, Electroencephalography, Female, Humans, Male, Middle Aged, Signal Processing, Computer-Assisted, Verbal Behavior}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2010.10.029}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21029784}, author = {Pei, Xiao-Mei and Leuthardt, E C and Charles M Gaona and Peter Brunner and Jonathan Wolpaw and Gerwin Schalk} } @article {2131, title = {Transition from the locked in to the completely locked-in state: a physiological analysis.}, journal = {Clin Neurophysiol}, volume = {122}, year = {2011}, month = {06/2011}, pages = {925-33}, abstract = {

OBJECTIVE:\ 

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\ brain-computer-interface\ (BCI) communication.

METHODS:\ 

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.

RESULTS:\ 

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\ brainpotentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response.

CONCLUSIONS:\ 

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.

SIGNIFICANCE:\ 

Auditory and proprioceptive\ brain-computer-interface\ (BCI) systems are the only remaining communication channels in CLIS.

}, keywords = {Adult, Amyotrophic Lateral Sclerosis, Area Under Curve, Brain, Communication Aids for Disabled, Disease Progression, Electroencephalography, Electromyography, Humans, Male, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1872-8952}, doi = {10.1016/j.clinph.2010.08.019}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20888292}, author = {Murguialday, A Ramos and Jeremy Jeremy Hill and Bensch, M and Martens, S M M and S Halder and Nijboer, F and Schoelkopf, Bernhard and Niels Birbaumer and Gharabaghi, A} } @article {2206, title = {Using the electrocorticographic speech network to control a brain-computer interface in humans.}, journal = {J Neural Eng}, volume = {8}, year = {2011}, month = {06/2011}, pages = {036004}, abstract = {

Electrocorticography (ECoG) has emerged as a new signal platform for\ brain-computer interface\ (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been\ brain\ 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\ computer\ 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\ BCI\ operation and that these signals can be acquired from a cortical array that is small and minimally invasive.

}, keywords = {Adult, Brain, Brain Mapping, Computer Peripherals, Electroencephalography, Evoked Potentials, Feedback, Physiological, Female, Humans, Imagination, Male, Middle Aged, Nerve Net, Speech Production Measurement, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/8/3/036004}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21471638}, author = {Leuthardt, E C and Charles M Gaona and Sharma, Mohit and Szrama, Nicholas and Roland, Jarod and Zachary V. Freudenberg and Solisb, Jamie and Breshears, Jonathan and Gerwin Schalk} } @article {2194, title = {Brain-computer interfacing based on cognitive control.}, journal = {Ann Neurol}, volume = {67}, year = {2010}, month = {06/2010}, pages = {809-16}, abstract = {

OBJECTIVE:\ 

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.

METHODS:\ 

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).

RESULTS:\ 

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.

INTERPRETATION:\ 

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.

}, keywords = {Cognition, Computers, Electrodes, Electroencephalography, Epilepsy, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neuropsychological Tests, Oxygen, Prefrontal Cortex, Psychomotor Performance, Spectrum Analysis, Time Factors, User-Computer Interface}, issn = {1531-8249}, doi = {10.1002/ana.21985}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20517943}, author = {Vansteensel, Mariska J and Hermes, Dora and Aarnoutse, Erik J and Bleichner, Martin G and Gerwin Schalk and van Rijen, Peter C and Leijten, Frans S S and Ramsey, Nick F} } @article {2196, title = {Electrocorticographic frequency alteration mapping for extraoperative localization of speech cortex.}, journal = {Neurosurgery}, volume = {66}, year = {2010}, month = {02/2010}, pages = {E407-9}, abstract = {

OBJECTIVE:\ 

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.

METHODS:\ 

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.

RESULTS:\ 

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.

CONCLUSION:\ 

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.

}, keywords = {Acoustic Stimulation, Adolescent, Adult, Brain Mapping, Cerebral Cortex, Chi-Square Distribution, Electroencephalography, Epilepsy, Female, Humans, Male, Mass Spectrometry, Middle Aged, Photic Stimulation, Speech, Verbal Behavior, Young Adult}, issn = {1524-4040}, doi = {10.1227/01.NEU.0000345352.13696.6F}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20087111}, author = {Wu, Melinda and Wisneski, Kimberly and Gerwin Schalk and Sharma, Mohit and Roland, Jarod and Breshears, Jonathan and Charles M Gaona and Leuthardt, E C} } @article {2197, title = {Passive real-time identification of speech and motor cortex during an awake craniotomy.}, journal = {Epilepsy Behav}, volume = {18}, year = {2010}, month = {05/2010}, pages = {123-8}, abstract = {

Precise localization of eloquent cortex is a clinical necessity prior to surgical resections adjacent to speech or\ motor\ 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\ motor\ and speech tasks. SIGFRIED, a procedure for real-time event detection, was used to identify areas of\ cortical\ activation by detecting task-related modulations in the ECoG high gamma band. SIGFRIED{\textquoteright}s real-time output quickly localized\ motor\ and speech areas of cortex similar to those identified by DECS. In conclusion, real-time passive identification of\ cortical\ function using SIGFRIED may serve as a useful adjunct to\ cortical\ stimulation mapping in the intraoperative setting.

}, keywords = {Brain Mapping, Brain Neoplasms, Cerebral Cortex, Craniotomy, Electric Stimulation, Electroencephalography, Humans, Neurologic Examination}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2010.02.017}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20478745}, author = {Roland, Jarod and Peter Brunner and Johnston, James and Gerwin Schalk and Leuthardt, E C} } @article {2195, title = {A procedure for measuring latencies in brain-computer interfaces.}, journal = {IEEE Trans Biomed Eng}, volume = {57}, year = {2010}, month = {06/2010}, pages = {1785-97}, abstract = {

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.

}, keywords = {Brain, Computer Systems, Electroencephalography, Evoked Potentials, Humans, Models, Neurological, Reproducibility of Results, Signal Processing, Computer-Assisted, Time Factors, User-Computer Interface}, issn = {1558-2531}, doi = {10.1109/TBME.2010.2047259}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20403781}, author = {Adam J Wilson and Mellinger, J{\"u}rgen and Gerwin Schalk and Williams, Justin C} } @article {2201, title = {Proceedings of the first international workshop on advances in electrocorticography.}, journal = {Epilepsy Behav}, volume = {19}, year = {2010}, month = {10/2010}, pages = {204-15}, abstract = {

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.

}, keywords = {Brain, Brain Mapping, Diagnosis, Computer-Assisted, Electroencephalography, Humans, International Cooperation, Seizures, Signal Detection, Psychological}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2010.08.028}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20889384}, author = {A L Ritaccio and Peter Brunner and Cervenka, Mackenzie C and Nathan E. Crone and Guger, C and Leuthardt, E C and Oostenveld, Robert and Stacey, William and Gerwin Schalk} } @article {2242, title = {Advances in the application of technology to epilepsy: the CIMIT/NIO Epilepsy Innovation Summit.}, journal = {Epilepsy Behav}, volume = {16}, year = {2009}, month = {09/2009}, pages = {3-46}, abstract = {

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.

}, keywords = {Adult, Anticonvulsants, Brain Mapping, Child, Drug Resistance, Electric Stimulation Therapy, Electroencephalography, Engineering, Epilepsy, Humans, Magnetic Resonance Imaging, Medical Laboratory Science, Microelectrodes, Nanoparticles, Neurons, Neurosurgery, Neurotoxins, Predictive Value of Tests, Seizures, Spectroscopy, Near-Infrared, Tomography, Emission-Computed, Single-Photon, Tomography, Optical, Transcranial Magnetic Stimulation}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2009.06.028}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19780225}, author = {Schachter, Steven C and Guttag, John and Schiff, Steven J and Schomer, Donald L} } @article {2136, title = {Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.}, journal = {J Neural Eng}, volume = {6}, year = {2009}, month = {04/2009}, pages = {026003}, abstract = {

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.

}, keywords = {Algorithms, Brain, Cognition, Computer Simulation, Electroencephalography, Event-Related Potentials, P300, Humans, Models, Neurological, Pattern Recognition, Automated, Photic Stimulation, Semantics, Signal Processing, Computer-Assisted, Task Performance and Analysis, User-Computer Interface, Writing}, issn = {1741-2552}, doi = {10.1088/1741-2560/6/2/026003}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19255462}, author = {Martens, S M M and Jeremy Jeremy Hill and Farquhar, Jason and Sch{\"o}lkopf, B} } @article {2152, title = {Power spectrum analysis on the multiparameter electroencephalogram features of physiological mental fatigue.}, journal = {Sheng Wu Yi Xue Gong Cheng Xue Za Zhi}, volume = {26}, year = {2009}, month = {02/2009}, pages = {162-6, 172}, abstract = {

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{\textquoteright} 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.

}, keywords = {Adult, Electroencephalography, Entropy, Humans, Male, Mental Fatigue, Signal Processing, Computer-Assisted, Young Adult}, issn = {1001-5515}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19334577}, author = {Zhang, Ai-hua and Zheng, Shi Dong and Pei, Xiao-Mei and Ouyang, Yi} } @article {2192, title = {A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans.}, journal = {Epilepsy Behav}, volume = {15}, year = {2009}, month = {07/2009}, pages = {278-86}, abstract = {

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.

}, keywords = {Adult, Brain Mapping, Cerebral Cortex, Electric Stimulation, Electrodes, Implanted, Electroencephalography, Epilepsy, Female, Humans, Male, Middle Aged, Practice Guidelines as Topic, Signal Processing, Computer-Assisted, Young Adult}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2009.04.001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19366638}, author = {Peter Brunner and A L Ritaccio and Lynch, Timothy M and Emrich, Joseph F and Adam J Wilson and Williams, Justin C and Aarnoutse, Erik J and Ramsey, Nick F and Leuthardt, E C and H Bischof and Gerwin Schalk} } @article {2845, title = {Seizure prediction for epilepsy using a multi-stage phase synchrony based system.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {2009}, year = {2009}, month = {09/2009}, pages = {25-8}, abstract = {Seizure 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.}, keywords = {Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity}, issn = {1557-170X}, doi = {10.1109/IEMBS.2009.5334898}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19965104}, author = {Christopher J James and Disha Gupta} } @article {2190, title = {Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000.}, journal = {J Vis Exp}, year = {2009}, month = {07/2009}, abstract = {

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].

}, keywords = {Brain, Calibration, Electrodes, Electroencephalography, Humans, User-Computer Interface}, issn = {1940-087X}, doi = {10.3791/1319}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19641479}, author = {Adam J Wilson and Gerwin Schalk and Walton, L{\'e}o M and Williams, Justin C} } @article {2189, title = {Advanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities.}, journal = {J Neurosci}, volume = {28}, year = {2008}, month = {11/2008}, pages = {11830-8}, keywords = {Cerebral Cortex, Electrodes, Implanted, Electroencephalography, Electronics, Medical, Electrophysiology, Evoked Potentials, Movement Disorders, Neurons, Prostheses and Implants, User-Computer Interface}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3879-08.2008}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19005048?report=abstract}, author = {Kipke, Daryl R and Shain, William and Buzs{\'a}ki, Gy{\"o}rgy and Fetz, Eberhard E and Henderson, Jaimie M and Hetke, Jamille F and Gerwin Schalk} } @article {2183, title = {Brain-computer interfaces (BCIs): Detection Instead of Classification.}, journal = {J Neurosci Methods}, volume = {167}, year = {2008}, month = {01/2008}, pages = {51-62}, abstract = {

Many studies over the past two decades have shown that people can use\ brain\ signals to convey their intent to a\ computer\ through\ brain-computer interfaces\ (BCIs). These devices operate by recording signals from the\ brain\ 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\ clinical\ applications is the current requirement for preliminary analyses to identify the\ brain\ 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\ brain\ activity so that any change in\ relevantbrain\ 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\ brain\ 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.

}, keywords = {Adult, Algorithms, Brain, Brain Mapping, Electrocardiography, Electroencephalography, Humans, Male, Man-Machine Systems, Normal Distribution, Online Systems, Signal Detection, Psychological, Signal Processing, Computer-Assisted, Software Validation, User-Computer Interface}, issn = {0165-0270}, doi = {10.1016/j.jneumeth.2007.08.010}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17920134}, author = {Gerwin Schalk and Peter Brunner and Lester A Gerhardt and H Bischof and Jonathan Wolpaw} } @article {2107, title = {Electrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {2008}, year = {2008}, month = {08/2008}, pages = {5224-7}, abstract = {

Electrocorticographic (ECoG) neuroprosthesis is a promising area of research that could provide channels of communication and control for patients who have lost their motor functions due to damage to the nervous system. However, implantation of subdural electrodes are clinically restricted to diagnostics of pre-surgical epileptic patients. Hence, interictal activity is present in the recordings across various areas of the sensorimotor cortex and suppresses the amplitude modulated features extracted to model hand trajectories. Denoising source separation is a recently introduced framework which extracts hidden structures of interest within the\ data\ through denoising the source estimates with filters designed around prior knowledge on the observations. Herein, we exploit the high amplitude quasiperiodic nature of the observed interictal spikes and show that removal of the interictal activity improves linear prediction of hand trajectories.

}, keywords = {Algorithms, Artifacts, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Evoked Potentials, Motor, Motor Cortex, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {1557-170X}, doi = {10.1109/IEMBS.2008.4650392}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19163895}, author = {Gunduz, Aysegul and Sanchez, Justin C and Principe, Jose} } @article {2098, title = {Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.}, journal = {J Neurosci Methods}, volume = {167}, year = {2008}, month = {01/2008}, pages = {63-81}, abstract = {

Electrocorticogram (ECoG) recordings for\ neuroprosthetics\ provide a\ mesoscopic\ level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial\ ECoG\ neural interfaces require appropriate feature\ extraction\ and signal processing methods to identify and model in real-time signatures of\ motor\ events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz)\ ECoG\ 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\ ECoG\ voltages to determine if they provide viable\ control\ inputs for\ ECoG\ neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the\ ECoG\ above the level of noisy background fluctuations in order to derive the commands for complex, continuous\ control\ tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for\ motor\ tasks using amplitude modulated\ ECoG.

}, keywords = {Adolescent, Biofeedback, Psychology, Brain Mapping, Cerebral Cortex, Electroencephalography, Epilepsies, Partial, Female, Hand, Humans, Magnetic Resonance Imaging, Physical Therapy Modalities, Psychomotor Performance, Signal Processing, Computer-Assisted, Spectrum Analysis, User-Computer Interface}, issn = {0165-0270}, doi = {10.1016/j.jneumeth.2007.04.019}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17582507}, author = {Sanchez, Justin C and Gunduz, Aysegul and Carney, Paul R and Principe, Jose} } @article {2185, title = {Non-invasive brain-computer interface system: towards its application as assistive technology.}, journal = {Brain Res Bull}, volume = {75}, year = {2008}, month = {04/2008}, pages = {796-803}, abstract = {

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{\textquoteright}s residual motor abilities.\ Brain-computer interfaces\ (BCIs) are systems that can translate\ brain\ 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{\textquoteright}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{\textquoteright} 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-clinical\ demonstrations to a\ clinical\ useful BCI.

}, keywords = {Activities of Daily Living, Adolescent, Adult, Brain, Child, Electroencephalography, Evoked Potentials, Motor, Female, Humans, Learning, Male, Middle Aged, Motor Skills, Muscular Dystrophy, Duchenne, Pilot Projects, Prostheses and Implants, Robotics, Self-Help Devices, Software, Spinal Muscular Atrophies of Childhood, User-Computer Interface, Volition}, issn = {0361-9230}, doi = {10.1016/j.brainresbull.2008.01.007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18394526}, author = {Cincotti, F and Mattia, Donatella and Aloise, Fabio and Bufalari, Simona and Gerwin Schalk and Oriolo, Giuseppe and Cherubini, Andrea and Marciani, Maria Grazia and Babiloni, Fabio} } @article {2187, title = {Real-time detection of event-related brain activity.}, journal = {Neuroimage}, volume = {43}, year = {2008}, month = {11/2008}, pages = {245-9}, abstract = {

The complexity and inter-individual variation of\ brain\ 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\ brain\ 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\ brain\ function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize\ brain\ 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.

}, keywords = {Adult, Algorithms, Brain Mapping, Computer Systems, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Evoked Potentials, Female, Humans, Male, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2008.07.037}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18718544}, author = {Gerwin Schalk and Leuthardt, E C and Peter Brunner and Ojemann, J G and Lester A Gerhardt and Jonathan Wolpaw} } @conference {3438, title = {Three cases of feature correlation in an electrocorticographic BCI.}, booktitle = {Engineering in Medicine and Biology Society, 2008.}, year = {2008}, month = {08/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Vancouver, BC}, abstract = {Three 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.}, keywords = {Adolescent, Adult, Algorithms, automated pattern recognition, control systems, decorrelation, Electrocardiography, Electrodes, Electroencephalography, evoked motor potentials, Feedback, Female, frequency, hospitals, Humans, Male, Middle Aged, Motor Cortex, Signal Processing, Statistics as Topic, Task Performance and Analysis, Tongue, User-Computer Interface}, doi = {10.1109/IEMBS.2008.4650415}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19163918}, author = {Miller, Kai J and Blakely, Timothy and Gerwin Schalk and den Nijs, Marcel and Rao, Rajesh PN and Ojemann, Jeffrey G} } @article {2184, title = {Towards an independent brain-computer interface using steady state visual evoked potentials.}, journal = {Clin Neurophysiol}, volume = {119}, year = {2008}, month = {02/2008}, pages = {399-408}, abstract = {

OBJECTIVE:\ 

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.

METHODS:\ 

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.

RESULTS:\ 

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.

CONCLUSIONS:\ 

The\ data\ 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{\textquoteright}s dependence on muscle activity is a function of many factors, including the display, task, environment, and user.

SIGNIFICANCE:\ 

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.

}, keywords = {Adolescent, Adult, Attention, Brain, Brain Mapping, Dose-Response Relationship, Radiation, Electroencephalography, Evoked Potentials, Visual, Female, Humans, Male, Pattern Recognition, Visual, Photic Stimulation, Spectrum Analysis, User-Computer Interface}, issn = {1388-2457}, doi = {10.1016/j.clinph.2007.09.121}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18077208}, author = {Brendan Z. Allison and Dennis J. McFarland and Gerwin Schalk and Zheng, Shi Dong and Moore-Jackson, Melody and Jonathan Wolpaw} } @article {2186, title = {Two-dimensional movement control using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {5}, year = {2008}, month = {03/2008}, pages = {75-84}, abstract = {

We show here that a\ brain-computer\ interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a\ computer\ 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\ clinical\ practicality, and also indicate promising directions for further research.

}, keywords = {Adolescent, Adult, Brain Mapping, Data Interpretation, Statistical, Drug Resistance, Electrocardiography, Electrodes, Implanted, Electroencephalography, Epilepsy, Female, Humans, Male, Movement, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/5/1/008}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18310813}, author = {Gerwin Schalk and Miller, K.J. and Nicholas R Anderson and Adam J Wilson and Smyth, Matt and Ojemann, J G and Moran, D and Jonathan Wolpaw and Leuthardt, E C} } @article {2188, title = {Unique cortical physiology associated with ipsilateral hand movements and neuroprosthetic implications.}, journal = {Stroke}, volume = {39}, year = {2008}, month = {12/2008}, pages = {3351-9}, abstract = {

BACKGROUND AND PURPOSE:\ 

Brain computer interfaces\ (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.

METHODS:\ 

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.

RESULTS:\ 

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.

CONCLUSIONS:\ 

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.

}, keywords = {Adolescent, Adult, Artificial Limbs, Bionics, Brain Mapping, Child, Dominance, Cerebral, Electroencephalography, Female, Hand, Humans, Male, Middle Aged, Motor Cortex, Movement, Paresis, Prosthesis Design, Psychomotor Performance, Stroke, User-Computer Interface, Volition}, issn = {1524-4628}, doi = {10.1161/STROKEAHA.108.518175}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18927456}, author = {Wisneski, Kimberly and Nicholas R Anderson and Gerwin Schalk and Smyth, Matt and Moran, D and Leuthardt, E C} } @article {2137, title = {Voluntary brain regulation and communication with electrocorticogram signals.}, journal = {Epilepsy Behav}, volume = {13}, year = {2008}, month = {08/2008}, pages = {300-6}, abstract = {

Brain-computer interfaces\ (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of\ brain\ 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\ computer-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.

}, keywords = {Adult, Biofeedback, Psychology, Cerebral Cortex, Communication Aids for Disabled, Dominance, Cerebral, Electroencephalography, Epilepsies, Partial, Female, Humans, Imagination, Male, Middle Aged, Motor Activity, Motor Cortex, Signal Processing, Computer-Assisted, Software, Somatosensory Cortex, Theta Rhythm, User-Computer Interface, Writing}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2008.03.014}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18495541}, author = {Hinterberger, T. and Widman, Guido and Lal, T.N and Jeremy Jeremy Hill and Tangermann, Michael and Rosenstiel, W. and Sch{\"o}lkopf, B and Elger, Christian and Niels Birbaumer} } @article {2182, title = {Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {4}, year = {2007}, month = {09/2007}, pages = {264-75}, abstract = {

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.

}, keywords = {Adult, Algorithms, Arm, Brain Mapping, Cerebral Cortex, Electroencephalography, Evoked Potentials, Motor, Female, Humans, Male, Movement}, issn = {1741-2560}, doi = {10.1088/1741-2560/4/3/012}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17873429}, author = {Gerwin Schalk and Kub{\'a}nek, J and Miller, John W and Nicholas R Anderson and Leuthardt, E C and Ojemann, J G and Limbrick, D and Moran, D and Lester A Gerhardt and Jonathan Wolpaw} } @article {2179, title = {Electrocorticographic Frequency Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex.}, journal = {Neurosurgery}, volume = {60}, year = {2007}, month = {04/2007}, pages = {260-70; discussion 270-1}, abstract = {

OBJECTIVE:\ 

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.

METHODS:\ 

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.

RESULTS:\ 

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.

CONCLUSION:\ 

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.

}, keywords = {Adult, Biological Clocks, Brain Mapping, Electric Stimulation, Electrodes, Implanted, Electroencephalography, Female, Hand, Humans, Male, Middle Aged, Motor Cortex, Oscillometry, Signal Processing, Computer-Assisted, Tongue}, issn = {1524-4040}, doi = {10.1227/01.NEU.0000255413.70807.6E}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17415162}, author = {Leuthardt, E C and Miller, John W and Nicholas R Anderson and Gerwin Schalk and Dowling, Joshua and Miller, John W and Moran, D and Ojemann, J G} } @article {2181, title = {An MEG-based brain-computer interface (BCI).}, journal = {Neuroimage}, volume = {36}, year = {2007}, month = {07/2007}, pages = {581-93}, abstract = {

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\ electroencephalography(EEG) 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\ EEG\ 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.

}, keywords = {Adult, Algorithms, Artifacts, Brain, Electroencephalography, Electromagnetic Fields, Electromyography, Feedback, Female, Foot, Hand, Head Movements, Humans, Magnetic Resonance Imaging, Magnetoencephalography, Male, Movement, Principal Component Analysis, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1053-8119}, doi = {10.1016/j.neuroimage.2007.03.019}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17475511}, author = {Mellinger, J{\"u}rgen and Gerwin Schalk and Christoph Braun and Preissl, Hubert and Rosenstiel, W. and Niels Birbaumer and K{\"u}bler, A.} } @article {2844, title = {Narrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {2007}, year = {2007}, month = {08/2007}, pages = {3864-7}, abstract = {This 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.}, keywords = {Algorithms, Electroencephalography, Humans, Predictive Value of Tests, Seizures, Signal Processing, Computer-Assisted}, issn = {1557-170X}, doi = {10.1109/IEMBS.2007.4353176}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18002842}, author = {Disha Gupta and Christopher J James} } @article {2846, title = {Space-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {08/2007}, year = {2007}, month = {2007}, pages = {5473-6}, abstract = {In 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.}, keywords = {Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, Pattern Recognition, Automated, Principal Component Analysis, Reproducibility of Results, Sensitivity and Specificity}, issn = {1557-170X}, doi = {10.1109/IEMBS.2007.4353584}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18003250}, author = {Christopher J James and Ab{\'a}solo, Daniel and Disha Gupta} } @article {2174, title = {A {\textmu}-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface.}, journal = {IEEE Trans Biomed Eng}, volume = {54}, year = {2007}, month = {02/2007}, pages = {273-80}, abstract = {

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 (EEG) 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\ continuousamplitude 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.

}, keywords = {Algorithms, Cerebral Cortex, Cortical Synchronization, Electroencephalography, Evoked Potentials, Humans, Imagination, Pattern Recognition, Automated, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2006.886661}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17278584}, author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland and Jonathan Wolpaw} } @article {2172, title = {The BCI competition III: Validating alternative approaches to actual BCI problems.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {153-9}, abstract = {

A\ brain-computer interface\ (BCI) is a system that allows its users to control external devices with\ brainactivity. 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{\textquoteright}s\ brain, which produces\ brain\ 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\ online\ 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.

}, keywords = {Algorithms, Brain, Communication Aids for Disabled, Databases, Factual, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, Software Validation, Technology Assessment, Biomedical, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875642}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792282}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Krusienski, Dean J and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Pfurtscheller, Gert and Mill{\'a}n, Jos{\'e} del R and Schr{\"o}der, Michael and Niels Birbaumer} } @article {2176, title = {BCI meeting 2005 - Workshop on Technology: Hardware and Software.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {128-31}, abstract = {

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{\textquoteright} needs and the need for consistent standards in BCI research.

}, keywords = {Algorithms, Biotechnology, Brain, Communication Aids for Disabled, Computers, Electroencephalography, Equipment Design, Humans, Internationality, Man-Machine Systems, Neuromuscular Diseases, Software, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875584}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792276}, author = {Cincotti, F and Bianchi, L and Birch, Gary and Guger, C and Mellinger, J{\"u}rgen and Scherer, Reinhold and Schmidt, Robert N and Y{\'a}{\~n}ez Su{\'a}rez, Oscar and Gerwin Schalk} } @article {2143, title = {Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {183-6}, abstract = {

We summarize results from a series of related studies that aim to develop a motor-imagery-basedbrain-computer interface\ 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.

}, keywords = {Algorithms, Artificial Intelligence, Cluster Analysis, Computer User Training, Electroencephalography, Evoked Potentials, Female, Humans, Imagination, Male, Middle Aged, Paralysis, Pattern Recognition, Automated, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875548}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792289}, author = {Jeremy Jeremy Hill and Lal, T.N and Schr{\"o}der, Michael and Hinterberger, T. and Wilhelm, Barbara and Nijboer, F and Mochty, Ursula and Widman, Guido and Elger, Christian and Sch{\"o}lkopf, B and K{\"u}bler, A. and Niels Birbaumer} } @article {2173, title = {Electrocorticography-based brain computer interface--the Seattle experience.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {194-8}, abstract = {

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.

}, keywords = {Cerebral Cortex, Electroencephalography, Epilepsy, Evoked Potentials, Humans, Therapy, Computer-Assisted, User-Computer Interface, Washington}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875536}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792292}, author = {Leuthardt, E C and Miller, John W and Gerwin Schalk and Rao, Rajesh P N and Ojemann, J G} } @article {2153, title = {Multi-channel linear descriptors for event-related EEG collected in brain computer interface.}, journal = {J Neural Eng}, volume = {3}, year = {2006}, month = {03/2006}, pages = {52-8}, abstract = {

By three multi-channel linear descriptors, i.e. spatial\ complexity\ (omega), field power (sigma) and frequency of field changes (phi),\ event-relatedEEGdata\ within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the\ event-relatedEEGdata\ 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\ event-related\ 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\ event-relatedEEG. 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\ EEG\ patterns in the application of brain computer interfaces.

}, keywords = {Algorithms, Electroencephalography, Evoked Potentials, Motor, Humans, Imagination, Motor Cortex, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/3/1/006}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16510942}, author = {Pei, Xiao-Mei and Zheng, Shi Dong and Xu, Jin and Bin, Guang-yu and Zuoguan Wang} } @article {2177, title = {The Wadsworth BCI Research and Development Program: At Home with BCI.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {229-33}, abstract = {

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,\ electroencephalography\ (EEG)-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\ complexity\ 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{\textquoteright}s homes.

}, keywords = {Animals, Brain, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, New York, Research, Switzerland, Therapy, Computer-Assisted, Universities, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875577}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792301}, author = {Theresa M Vaughan and Dennis J. McFarland and Gerwin Schalk and Sarnacki, William A and Krusienski, Dean J and Sellers, Eric W and Jonathan Wolpaw} } @article {3138, title = {Brain-computer interface (BCI) operation: signal and noise during early training sessions.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {116}, year = {2005}, month = {01/2005}, pages = {56{\textendash}62}, abstract = {OBJECTIVE: People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the electroencephalogram (EEG) recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The recorded signal may also contain electromyogram (EMG) and other non-EEG artifacts. This study examines the presence and characteristics of EMG contamination during new users{\textquoteright} initial brain-computer interface (BCI) training sessions, as they first attempt to acquire control over mu or beta rhythm amplitude and to use that control to move a cursor to a target. METHODS: In the standard one-dimensional format, a target appears along the right edge of the screen and 1s later the cursor appears in the middle of the left edge and moves across the screen at a fixed rate with its vertical movement controlled by a linear function of mu or beta rhythm amplitude. In the basic two-choice version, the target occupies the upper or lower half of the right edge. The user{\textquoteright}s task is to move the cursor vertically so that it hits the target when it reaches the right edge. The present data comprise the first 10 sessions of BCI training from each of 7 users. Their data were selected to illustrate the variations seen in EMG contamination across users. RESULTS: Five of the 7 users learned to change rhythm amplitude appropriately, so that the cursor hit the target. Three of these 5 showed no evidence of EMG contamination. In the other two of these 5, EMG was prominent in early sessions, and tended to be associated with errors rather than with hits. As EEG control improved over the 10 sessions, this EMG contamination disappeared. In the remaining two users, who never acquired actual EEG control, EMG was prominent in initial sessions and tended to move the cursor to the target. This EMG contamination was still detectable by Session 10. CONCLUSIONS: EMG contamination arising from cranial muscles is often present early in BCI training and gradually wanes. In those users who eventually acquire EEG control, early target-related EMG contamination may be most prominent for unsuccessful trials, and may reflect user frustration. In those users who never acquire EEG control, EMG may initially serve to move the cursor toward the target. Careful and comprehensive topographical and spectral analyses throughout user training are essential for detecting EMG contamination and differentiating between cursor control provided by EEG control and cursor control provided by EMG contamination. SIGNIFICANCE: Artifacts such as EMG are common in EEG recordings. Comprehensive spectral and topographical analyses are necessary to detect them and ensure that they do not masquerade as, or interfere with acquisition of, actual EEG-based cursor control.}, keywords = {brain-computer interface, EEG, Electroencephalography, Learning, mu rhythm, sensorimotor cortex}, issn = {1388-2457}, doi = {10.1016/j.clinph.2004.07.004}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15589184}, author = {Dennis J. McFarland and Sarnacki, William A. and Theresa M Vaughan and Jonathan Wolpaw} } @article {2169, title = {Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface.}, journal = {Neurology}, volume = {64}, year = {2005}, month = {05/2005}, pages = {1775-7}, abstract = {

People with severe motor disabilities can maintain an acceptable quality of life if they can communicate.\ Brain-computer interfaces\ (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-based\ BCI could help maintain quality of life for people with ALS.

}, keywords = {Aged, Amyotrophic Lateral Sclerosis, Electroencephalography, Evoked Potentials, Motor, Evoked Potentials, Somatosensory, Female, Humans, Imagination, Male, Middle Aged, Motor Cortex, Movement, Paralysis, Photic Stimulation, Prostheses and Implants, Somatosensory Cortex, Treatment Outcome, User-Computer Interface}, issn = {1526-632X}, doi = {10.1212/01.WNL.0000158616.43002.6D}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15911809}, author = {K{\"u}bler, A. and Nijboer, F and Mellinger, J{\"u}rgen and Theresa M Vaughan and Pawelzik, H and Gerwin Schalk and Dennis J. McFarland and Niels Birbaumer and Jonathan Wolpaw} } @article {2144, title = {Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces.}, journal = {EURASIP Journal on Advances in Signal Processing}, volume = {2005}, year = {2005}, month = {01/2005}, pages = {3103{\textendash}3112}, abstract = {

Most EEG-based brain-computer interface (BCI) paradigms come along with specific electrode positions, for example, for a visual-based BCI, electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects, Lal et al. in 2004 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extent their method of recursive channel elimination (RCE) can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view) were consistently selected whereas task-irrelevant channels were reliably disregarded.

}, keywords = {brain-computer interface, channel selection, Electroencephalography, feature selection, recursive channel elimination, support vector machine}, doi = {10.1155/ASP.2005.3103}, url = {http://www.researchgate.net/publication/26532072_Robust_EEG_Channel_Selection_across_Subjects_for_Brain-Computer_Interfaces}, author = {Schr{\"o}der, Michael and Lal, T.N and Hinterberger, T. and Bogdan, Martin and Jeremy Jeremy Hill and Niels Birbaumer and Rosenstiel, W. and Sch{\"o}lkopf, B}, editor = {Vesin J M, T EbrahimiEditor} } @conference {3436, title = {Tracking of the mu rhythm using an empirically derived matched filter.}, booktitle = {Proc. IEEE International Conference of Neural Engineering}, year = {2005}, month = {03/2005}, publisher = {IEEE}, organization = {IEEE}, address = {Arlington, VA}, keywords = {bioelectric potentials, Brain Computer Interfaces, brain modeling, brain-computer interface, communication device, communication system control, cortical mu rhythm modulation, EEG, Electroencephalography, empirically derived matched filter, handicapped aids, laboratories, matched filters, medical signal detection, medical signal processing, monitoring, motor imagery, mu rhythm tracking, noninvasive treatment, rhythm, synchronous motors, two-dimensional cursor control data}, isbn = {0-7803-8710-4}, doi = {10.1109/CNE.2005.1419559}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1419559}, author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland and Jonathan Wolpaw} } @article {2167, title = {The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials.}, journal = {IEEE Trans Biomed Eng}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1044-51}, abstract = {Interest 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.}, keywords = {Adult, Algorithms, Amyotrophic Lateral Sclerosis, Artificial Intelligence, Brain, Cognition, Databases, Factual, Electroencephalography, Evoked Potentials, Humans, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2004.826692}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, T. and Schr{\"o}der, Michael and Niels Birbaumer} } @article {2166, title = {BCI2000: a general-purpose brain-computer interface (BCI) system.}, journal = {IEEE Trans Biomed Eng}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1034-43}, abstract = {Many 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.}, keywords = {Algorithms, Brain, Cognition, Communication Aids for Disabled, Computer Peripherals, Electroencephalography, Equipment Design, Equipment Failure Analysis, Evoked Potentials, Humans, Systems Integration, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2004.827072}, author = {Gerwin Schalk and Dennis J. McFarland and Hinterberger, T. and Niels Birbaumer and Jonathan Wolpaw} } @article {2168, title = {A brain-computer interface using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {1}, year = {2004}, month = {06/2004}, pages = {63-71}, abstract = {

Brain-computer interfaces\ (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the\ brain. 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\ brain\ can enable users to control a one-dimensional\ computer\ 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\ brain.

}, keywords = {Adult, Brain, Communication Aids for Disabled, Computer Peripherals, Diagnosis, Computer-Assisted, Electrodes, Implanted, Electroencephalography, Evoked Potentials, Female, Humans, Imagination, Male, Movement Disorders, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/1/2/001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15876624}, author = {Leuthardt, E C and Gerwin Schalk and Jonathan Wolpaw and Ojemann, J G and Moran, D} } @article {3139, title = {Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {101}, year = {2004}, month = {12/2004}, pages = {17849{\textendash}17854}, abstract = {Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user{\textquoteright}s commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.}, keywords = {brain-machine interface, Electroencephalography}, issn = {0027-8424}, doi = {10.1073/pnas.0403504101}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15585584}, author = {Jonathan Wolpaw and Dennis J. McFarland} } @article {3140, title = {Conversion of EEG activity into cursor movement by a brain-computer interface (BCI).}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {12}, year = {2004}, month = {09/2004}, pages = {331{\textendash}338}, abstract = {The Wadsworth electroencephalogram (EEG)-based brain-computer interface (BCI) uses amplitude in mu or beta frequency bands over sensorimotor cortex to control cursor movement. Trained users can move the cursor in one or two dimensions. The primary goal of this research is to provide a new communication and control option for people with severe motor disabilities. Currently, cursor movements in each dimension are determined 10 times/s by an empirically derived linear function of one or two EEG features (i.e., spectral bands from different electrode locations). This study used offline analysis of data collected during system operation to explore methods for improving the accuracy of cursor movement. The data were gathered while users selected among three possible targets by controlling vertical [i.e., one-dimensional (1-D)] cursor movement. The three methods analyzed differ in the dimensionality of the cursor movement [1-D versus two-dimensional (2-D)] and in the type of the underlying function (linear versus nonlinear). We addressed two questions: Which method is best for classification (i.e., to determine from the EEG which target the user wants to hit)? How does the number of EEG features affect the performance of each method? All methods reached their optimal performance with 10-20 features. In offline simulation, the 2-D linear method and the 1-D nonlinear method improved performance significantly over the 1-D linear method. The 1-D linear method did not do so. These offline results suggest that the 1-D nonlinear or the 2-D linear cursor function will improve online operation of the BCI system.}, keywords = {augmentative communication, Brain-computer interface (BCI), Electroencephalography, Feedback}, issn = {1534-4320}, doi = {10.1109/TNSRE.2004.834627}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15473195}, author = {Fabiani, Georg E. and Dennis J. McFarland and Jonathan Wolpaw and Pfurtscheller, Gert} } @article {3144, title = {Brain-computer interface (BCI) operation: optimizing information transfer rates.}, journal = {Biological psychology}, volume = {63}, year = {2003}, month = {07/2003}, pages = {237{\textendash}251}, abstract = {People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. In the present version of the cursor movement task, vertical cursor movement is a linear function of mu or beta rhythm amplitude. At the same time the cursor moves horizontally from left to right at a fixed rate. A target occupies 50\% (2-target task) to 20\% (5-target task) of the right edge of the screen. The user{\textquoteright}s task is to move the cursor vertically so that it hits the target when it reaches the right edge. The goal of the present study was to optimize system performance. To accomplish this, we evaluated the impact on system performance of number of targets (i.e. 2-5) and trial duration (i.e. horizontal movement time from 1 to 4 s). Performance was measured as accuracy (percent of targets selected correctly) and also as bit rate (bits/min) (which incorporates, in addition to accuracy, speed and the number of possible targets). Accuracy declined as target number increased. At the same time, for six of eight users, four targets yielded the maximum bit rate. Accuracy increased as movement time increased. At the same time, the movement time with the highest bit rate varied across users from 2 to 4 s. These results indicate that task parameters such as target number and trial duration can markedly affect system performance. They also indicate that optimal parameter values vary across users. Selection of parameters suited both to the specific user and the requirements of the specific application is likely to be a key factor in maximizing the success of EEG-based communication and control.}, keywords = {augmentative communication, Electroencephalography, information, Learning, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0301-0511}, doi = {10.1016/S0301-0511(03)00073-5}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12853169}, author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw} } @article {3145, title = {Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans.}, journal = {Neuroscience letters}, volume = {345}, year = {2003}, month = {07/2002}, pages = {89{\textendash}92}, abstract = {People can learn to control electroencephalographic (EEG) sensorimotor rhythm amplitude so as to move a cursor to select among choices on a computer screen. We explored the dependence of system performance on EEG control. Users moved the cursor to reach a target at one of four possible locations. EEG control was measured as the correlation (r(2)) between rhythm amplitude and target location. Performance was measured as accuracy (\% of targets hit) and as information transfer rate (bits/trial). The relationship between EEG control and accuracy can be approximated by a linear function that is constant for all users. The results facilitate offline predictions of the effects on performance of using different EEG features or combinations of features to control cursor movement.}, keywords = {augmentative communication, brain-computer interface, brain-machine interface, Electroencephalography, mu and beta rhythms, neuroprosthesis, Rehabilitation}, issn = {0304-3940}, doi = {10.1016/S0304-3940(03)00470-1}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12821178}, author = {Sheikh, Hesham and Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw} } @article {3147, title = {Spectral dynamics of electroencephalographic activity during auditory information processing.}, journal = {Hearing research}, volume = {176}, year = {2003}, month = {02/2003}, pages = {25{\textendash}41}, abstract = {Dynamics of electroencephalographic (EEG) activity during auditory information processing were evaluated in response to changes in stimulus complexity, stimulus discriminability and attention using the oddball paradigm. In comparison to pre-stimulus baseline conditions, auditory stimulation synchronized EEG activity in delta, theta and alpha frequency bands. Event-related synchronization (ERS) effects were greatest at approximately 3 Hz (theta frequency band), and their magnitude depended on stimulus and task demands. Event-related desynchronization (ERD) of EEG activity was observed in the beta frequency band. This effect was greatest at approximately 21 Hz but occurred only for easily discriminable stimuli in attention-related target conditions. Because active discrimination tasks also required a button-press response with the right hand, ERDs involved more complex responses that may be related to a combination of perceptual, motor and cognitive processes. These results demonstrate that oddball and attention-related EEG responses to auditory stimulation could be characterized in the frequency domain. The specific design and analysis features described herein may prove useful since they provide a simple index of the brain{\textquoteright}s response to stimulation while at the same time provide powerful information not contained in typical time domain analysis.}, keywords = {Electroencephalography, event-related brain dynamics, event-related desynchronization, event-related synchronization, psychophysics, spectral analysis, time domain analysis}, issn = {0378-5955}, doi = {10.1016/S0378-5955(02)00715-3}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12583879}, author = {Anthony T. Cacace and Dennis J. McFarland} } @article {2165, title = {The Wadsworth Center brain-computer interface (BCI) research and development program.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {11}, year = {2003}, month = {06/2003}, pages = {204-7}, abstract = {

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\ control\ cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this\ control\ 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\ groups. Finally, in collaboration with several other\ groups, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.

}, keywords = {Academic Medical Centers, Adult, Algorithms, Artifacts, Brain, Brain Mapping, Electroencephalography, Evoked Potentials, Visual, Feedback, Humans, Middle Aged, Nervous System Diseases, Research, Research Design, User-Computer Interface, Visual Perception}, issn = {1534-4320}, doi = {10.1109/TNSRE.2003.814442}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12899275}, author = {Jonathan Wolpaw and Dennis J. McFarland and Theresa M Vaughan and Gerwin Schalk} } @article {2268, title = {Brain-computer interfaces for communication and control.}, journal = {Clin Neurophysiol}, volume = {113}, year = {2002}, month = {06/2002}, pages = {767-91}, abstract = {

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\ controltechnology 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 {\textquoteright}locked in{\textquoteright}, 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\ control, 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\ control\ independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that\ control; 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\ control\ 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\ control\ option for those with motor disabilities and might also give those without disabilities a supplementary\ control\ channel or a\ control\ channel useful in special circumstances.

}, keywords = {Brain Diseases, Communication Aids for Disabled, Computer Systems, Electroencephalography, Humans, User-Computer Interface}, issn = {1388-2457}, doi = {10.1016/S1388-2457(02)00057-3}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12048038}, author = {Jonathan Wolpaw and Niels Birbaumer and Dennis J. McFarland and Pfurtscheller, Gert and Theresa M Vaughan} } @article {2163, title = {Brain-computer interface technology: a review of the first international meeting.}, journal = {IEEE Trans Rehabil Eng}, volume = {8}, year = {2000}, month = {06/2000}, pages = {164-73}, abstract = {

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{\textquoteright}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{\textquoteright}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{\textquoteright}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.

}, keywords = {Algorithms, Cerebral Cortex, Communication Aids for Disabled, Disabled Persons, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1063-6528}, doi = {10.1109/TRE.2000.847807}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10896178}, author = {Jonathan Wolpaw and Niels Birbaumer and Heetderks, W J and Dennis J. McFarland and Peckham, P H and Gerwin Schalk and Emanuel Donchin and Quatrano, L A and Robinson, C J and Theresa M Vaughan} } @article {3234, title = {EEG-based communication: presence of an error potential.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {111}, year = {2000}, month = {12/2000}, pages = {2138{\textendash}2144}, abstract = {EEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect.}, keywords = {augmentative communication, brain-computer interface, Electroencephalography, error potential, error related negativity, event related potential, mu rhythm, Rehabilitation, sensorimotor cortex}, issn = {1388-2457}, doi = {10.1016/S1388-2457(00)00457-0}, url = {http://www.ncbi.nlm.nih.gov/pubmed/11090763}, author = {Gerwin Schalk and Jonathan Wolpaw and Dennis J. McFarland and Pfurtscheller, G.} } @article {3241, title = {EEG-based communication: analysis of concurrent EMG activity.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {107}, year = {1998}, month = {12/1998}, pages = {428{\textendash}433}, abstract = {OBJECTIVE: Recent studies indicate that people can learn to control the amplitude of mu or beta rhythms in the EEG recorded from the scalp over sensorimotor cortex and can use that control to move a cursor to targets on the computer screen. While subjects do not move during performance, it is possible that inapparent or unconscious muscle contractions contribute to the changes in the mu and beta rhythm activity responsible for cursor movement. We evaluated this possibility. METHODS: EMG was recorded from 10 distal limb muscle groups while five trained subjects used mu or beta rhythms to move a cursor to targets at the bottom or top edge of a computer screen. RESULTS: EMG activity was very low during performance, averaging 4.0+/-4.4\% (SD) of maximum voluntary contraction. Most important, the correlation, measured as r2, between target position and EMG activity averaged only 0.01+/-0.02, much lower than the correlation between target position and the EEG activity that controlled cursor movement, which averaged 0.39+/-0.18. CONCLUSIONS: These results strongly support the conclusion that EEG-based cursor control does no depend on concurrent muscle activity. EEG-based communication and control might provide a new augmentative communication option for those with severe motor disabilities.}, keywords = {augmentative communication, conditioning, Electroencephalography, Electromyography, mu rhythm, Rehabilitation, sensorimotor cortex}, issn = {0013-4694}, doi = {10.1016/S0013-4694(98)00107-2}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9922089}, author = {Theresa M Vaughan and Miner, L. A. and Dennis J. McFarland and Jonathan Wolpaw} } @article {3249, title = {Spatial filter selection for EEG-based communication.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {103}, year = {1997}, month = {09/1997}, pages = {386{\textendash}394}, abstract = {Individuals can learn to control the amplitude of mu-rhythm activity in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The speed and accuracy of cursor movement depend on the consistency of the control signal and on the signal-to-noise ratio achieved by the spatial and temporal filtering methods that extract the activity prior to its translation into cursor movement. The present study compared alternative spatial filtering methods. Sixty-four channel EEG data collected while well-trained subjects were moving the cursor to targets at the top or bottom edge of a video screen were analyzed offline by four different spatial filters, namely a standard ear-reference, a common average reference (CAR), a small Laplacian (3 cm to set of surrounding electrodes) and a large Laplacian (6 cm to set of surrounding electrodes). The CAR and large Laplacian methods proved best able to distinguish between top and bottom targets. They were significantly superior to the ear-reference method. The difference in performance between the large Laplacian and small Laplacian methods presumably indicated that the former was better matched to the topographical extent of the EEG control signal. The results as a whole demonstrate the importance of proper spatial filter selection for maximizing the signal-to-noise ratio and thereby improving the speed and accuracy of EEG-based communication.}, keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0013-4694}, doi = {10.1016/S0013-4694(97)00022-2}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9305287}, author = {Dennis J. McFarland and McCane, L. M. and David, S. V. and Jonathan Wolpaw} } @article {3245, title = {Timing of EEG-based cursor control.}, journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society}, volume = {14}, year = {1997}, month = {11/1997}, pages = {529{\textendash}538}, abstract = {Recent studies show that humans can learn to control the amplitude of electroencephalography (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could be a valuable new communication and control option for those with severe motor disabilities. Realization of this potential requires detailed knowledge of the characteristic features of EEG control. This study examined the course of EEG control after presentation of a target. At the beginning of each trial, a target appeared at the top or bottom edge of the subject{\textquoteright}s video screen and 1 sec later a cursor began to move vertically as a function of EEG amplitude in a specific frequency band. In well-trained subjects, this amplitude was high at the time the target appeared and then either remained high (i.e., for a top target) or fell rapidly (i.e., for a bottom target). Target-specific EEG amplitude control began 0.5 sec after the target appeared and appeared to wax and wane with a period of approximately 1 sec until the cursor reached the target (i.e., a hit) or the opposite edge of the screen (i.e., a miss). Accuracy was 90\% or greater for each subject. Top-target errors usually occurred later in the trial because of failure to reach and/or maintain sufficiently high amplitude, whereas bottom-target errors usually occurred immediately because of failure to reduce an initially high amplitude quickly enough. The results suggest modifications that could improve performance. These include lengthening the intertrial period, shortening the delay between target appearance and cursor movement, and including time within the trial as a variable in the equation that translates EEG into cursor movement.}, keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0736-0258}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9458060}, author = {Jonathan Wolpaw and Flotzinger, D. and Pfurtscheller, G. and Dennis J. McFarland} } @article {3262, title = {Multichannel EEG-based brain-computer communication.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {90}, year = {1994}, month = {06/1994}, pages = {444{\textendash}449}, abstract = {Individuals who are paralyzed or have other severe movement disorders often need alternative means for communicating with and controlling their environments. In this study, human subjects learned to use two channels of bipolar EEG activity to control 2-dimensional movement of a cursor on a computer screen. Amplitudes of 8-12 Hz activity in the EEG recorded from the scalp across right and left central sulci were determined by fast Fourier transform and combined to control vertical and horizontal cursor movements simultaneously. This independent control of two separate EEG channels cannot be attributed to a non-specific change in brain activity and appeared to be specific to the mu rhythm frequency range. With further development, multichannel EEG-based communication may prove of significant value to those with severe motor disabilities.}, keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0013-4694}, doi = {10.1016/0013-4694(94)90135-X}, url = {http://www.ncbi.nlm.nih.gov/pubmed/7515787}, author = {Jonathan Wolpaw and Dennis J. McFarland} }