02570nas a2200325 4500008004100000022001400041245013800055210006900193260001200262300001100274490000800285520150800293653002601801653002601827653001601853653001301869653001101882653003101893100002001924700002301944700002001967700002001987700001802007700002302025700001902048700001902067700002402086700001902110856011502129 2020 eng d a1873-423500aBreathable, large-area epidermal electronic systems for recording electromyographic activity during operant conditioning of H-reflex.0 aBreathable largearea epidermal electronic systems for recording c10/2020 a1124040 v1653 a
Operant conditioning of Hoffmann's reflex (H-reflex) is a non-invasive and targeted therapeutic intervention for patients with movement disorders following spinal cord injury. The reflex-conditioning protocol uses electromyography (EMG) to measure reflexes from specific muscles elicited using transcutaneous electrical stimulation. Despite recent advances in wearable electronics, existing EMG systems that measure muscle activity for operant conditioning of spinal reflexes still use rigid metal electrodes with conductive gels and aggressive adhesives, while requiring precise positioning to ensure reliability of data across experimental sessions. Here, we present the first large-area epidermal electronic system (L-EES) and demonstrate its use in every step of the reflex-conditioning protocol. The L-EES is a stretchable and breathable composite of nanomembrane electrodes (16 electrodes in a four by four array), elastomer, and fabric. The nanomembrane electrode array enables EMG recording from a large surface area on the skin and the breathable elastomer with fabric is biocompatible and comfortable for patients. We show that L-EES can record direct muscle responses (M-waves) and H-reflexes, both of which are comparable to those recorded using conventional EMG recording systems. In addition, L-EES may improve the reflex-conditioning protocol; it has potential to automatically optimize EMG electrode positioning, which may reduce setup time and error across experimental sessions.
10aBiosensing Techniques10aConditioning, Operant10aElectronics10aH-Reflex10aHumans10aReproducibility of Results1 aKwon, Young-Tae1 aNorton, James, J S1 aCutrone, Andrew1 aLim, Hyo-Ryoung1 aKwon, Shinjae1 aChoi, Jeongmoon, J1 aKim, Hee, Seok1 aJang, Young, C1 aWolpaw, Jonathan, R1 aYeo, Woon-Hong uhttps://www.neurotechcenter.org/publications/2020/breathable-large-area-epidermal-electronic-systems-recording02427nas a2200349 4500008004100000022001400041245011100055210006900166260001200235300001100247490000700258520132200265653001501587653002801602653003001630653003701660653002701697653002901724653001101753653001601764653001701780653001301797653003501810653003101845653003201876653004101908653002901949100001201978700001301990700002602003856004802029 2014 eng d a1558-021000aAdaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.0 aAdaptive spatiotemporal filtering for movement related potential c07/2014 a847-570 v223 aMovement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.10aAlgorithms10aArtificial Intelligence10abrain-computer interfaces10aData Interpretation, Statistical10aElectroencephalography10aEvoked Potentials, Motor10aHumans10aImagination10aMotor Cortex10aMovement10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity10aSignal Processing, Computer-Assisted10aSpatio-Temporal Analysis1 aLu, Jun1 aXie, Kan1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2472363203784nas a2200337 4500008004100000022001400041245006200055210006100117260001200178300001100190490000700201520283200208653001503040653001003055653002403065653002903089653001103118653002603129653001103155653000903166653002903175653002103204653003103225653003403256653002203290653001603312100002103328700002603349700002303375856004803398 2011 eng d a1050-054500aDichotic and dichoptic digit perception in normal adults.0 aDichotic and dichoptic digit perception in normal adults c06/2011 a332-410 v223 aBACKGROUND: Verbally based dichotic-listening experiments and reproduction-mediated response-selection strategies have been used for over four decades to study perceptual/cognitive aspects of auditory information processing and make inferences about hemispheric asymmetries and language lateralization in the brain. Test procedures using dichotic digits have also been used to assess for disorders of auditory processing. However, with this application, limitations exist and paradigms need to be developed to improve specificity of the diagnosis. Use of matched tasks in multiple sensory modalities is a logical approach to address this issue. Herein, we use dichotic listening and dichoptic viewing of visually presented digits for making this comparison. PURPOSE: To evaluate methodological issues involved in using matched tasks of dichotic listening and dichoptic viewing in normal adults. RESEARCH DESIGN: A multivariate assessment of the effects of modality (auditory vs. visual), digit-span length (1-3 pairs), response selection (recognition vs. reproduction), and ear/visual hemifield of presentation (left vs. right) on dichotic and dichoptic digit perception. STUDY SAMPLE: Thirty adults (12 males, 18 females) ranging in age from 18 to 30 yr with normal hearing sensitivity and normal or corrected-to-normal visual acuity. DATA COLLECTION AND ANALYSIS: A computerized, custom-designed program was used for all data collection and analysis. A four-way repeated measures analysis of variance (ANOVA) evaluated the effects of modality, digit-span length, response selection, and ear/visual field of presentation. RESULTS: The ANOVA revealed that performances on dichotic listening and dichoptic viewing tasks were dependent on complex interactions between modality, digit-span length, response selection, and ear/visual hemifield of presentation. Correlation analysis suggested a common effect on overall accuracy of performance but isolated only an auditory factor for a laterality index. CONCLUSIONS: The variables used in this experiment affected performances in the auditory modality to a greater extent than in the visual modality. The right-ear advantage observed in the dichotic-digits task was most evident when reproduction mediated response selection was used in conjunction with three-digit pairs. This effect implies that factors such as "speech related output mechanisms" and digit-span length (working memory) contribute to laterality effects in dichotic listening performance with traditional paradigms. Thus, the use of multiple-digit pairs to avoid ceiling effects and the application of verbal reproduction as a means of response selection may accentuate the role of nonperceptual factors in performance. Ideally, tests of perceptual abilities should be relatively free of such effects.10aAdolescent10aAdult10aAuditory Perception10aDichotic Listening Tests10aFemale10aFunctional Laterality10aHumans10aMale10aRecognition (Psychology)10aReference Values10aReproducibility of Results10aTask Performance and Analysis10aVisual Perception10aYoung Adult1 aLawfield, Angela1 aMcFarland, Dennis, J.1 aCacace, Anthony, T uhttp://www.ncbi.nlm.nih.gov/pubmed/2186447101606nas a2200301 4500008004100000022001400041245007000055210006600125260001200191300001200203490000700215520071500222653001000937653002100947653002700968653002200995653001101017653002501028653003101053653004101084653001701125653002801142100002001170700002301190700001901213700002401232856004801256 2010 eng d a1558-253100aA procedure for measuring latencies in brain-computer interfaces.0 aprocedure for measuring latencies in braincomputer interfaces c06/2010 a1785-970 v573 aBrain-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.
10aBrain10aComputer Systems10aElectroencephalography10aEvoked Potentials10aHumans10aModels, Neurological10aReproducibility of Results10aSignal Processing, Computer-Assisted10aTime Factors10aUser-Computer Interface1 aWilson, Adam, J1 aMellinger, Jürgen1 aSchalk, Gerwin1 aWilliams, Justin, C uhttp://www.ncbi.nlm.nih.gov/pubmed/2040378101983nas a2200373 4500008004100000022001400041245011800055210006900173260000900242300001100251490000900262520077500271653001501046653002401061653003001085653001101115653000901126653003501135653003201170653002301202653003101225653003201256653002801288653001801316100002001334700001701354700001901371700002001390700002401410700001701434700001701451700002101468856012001489 2009 eng d a1557-170X00aDetection of spontaneous class-specific visual stimuli with high temporal accuracy in human electrocorticography.0 aDetection of spontaneous classspecific visual stimuli with high c2009 a6465-80 v20093 aMost brain-computer interface classification experiments from electrical potential recordings have been focused on the identification of classes of stimuli or behavior where the timing of experimental parameters is known or pre-designated. Real world experience, however, is spontaneous, and to this end we describe an experiment predicting the occurrence, timing, and types of visual stimuli perceived by a human subject from electrocorticographic recordings. All 300 of 300 presented stimuli were correctly detected, with a temporal precision of order 20 ms. The type of stimulus (face/house) was correctly identified in 95% of these cases. There were approximately 20 false alarm events, corresponding to a late 2nd neuronal response to a previously identified event.10aAlgorithms10aElectrocardiography10aEvoked Potentials, Visual10aHumans10aMale10aPattern Recognition, Automated10aPattern Recognition, Visual10aPhotic Stimulation10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface10aVisual Cortex1 aMiller, John, W1 aHermes, Dora1 aSchalk, Gerwin1 aRamsey, Nick, F1 aJagadeesh, Bharathi1 aNijs, Marcel1 aOjemann, J G1 aRao, Rajesh, P N uhttps://www.neurotechcenter.org/publications/2009/detection-spontaneous-class-specific-visual-stimuli-high-temporal01326nas a2200265 4500008004100000022001400041245005600055210005400111260000900165300001300174490000900187520048800196653001500684653001000699653002400709653002100733653003100754653001900785653003100804653003200835653004100867653002800908100001900936856010500955 2009 eng d a1557-170X00aEffective brain-computer interfacing using BCI2000.0 aEffective braincomputer interfacing using BCI2000 c2009 a5498-5010 v20093 aTo facilitate research and development in Brain-Computer Interface (BCI) research, we have been developing a general-purpose BCI system, called BCI2000, over the past nine years. This system has enjoyed a growing adoption in BCI and related areas and has been the basis for some of the most impressive studies reported to date. This paper gives an update on the status of this project by describing the principles of the BCI2000 system, its benefits, and impact on the field to date.10aAlgorithms10aBrain10aElectrocardiography10aEquipment Design10aEquipment Failure Analysis10aRehabilitation10aReproducibility of Results10aSensitivity and Specificity10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/effective-brain-computer-interfacing-using-bci200001698nas a2200265 4500008004100000022001400041245008600055210006900141260001200210300000900222490000900231520087600240653001501116653002801131653003301159653002701192653001301219653001101232653003501243653003101278653003201309100002601341700001701367856004801384 2009 eng d a1557-170X00aSeizure prediction for epilepsy using a multi-stage phase synchrony based system.0 aSeizure prediction for epilepsy using a multistage phase synchro c09/2009 a25-80 v20093 aSeizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques. Here we present a multi-stage phase synchrony based system that brings to bear the advantages of many techniques in each substage. The 1(st) stage of the system unmixes continuous long-term (2-4 days) multichannel scalp EEG using spatially constrained Independent Component Analysis and estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. It then projects multidimensional features onto a 2-D map using Neuroscale and evaluates the probability of predictive events using Gaussian Mixture Models. We show the possibility of seizure onset prediction within a prediction window of 35-65 minutes with a sensitivity of 65-100% and specificity of 65-80% across epileptic patients.10aAlgorithms10aArtificial Intelligence10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aHumans10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity1 aJames, Christopher, J1 aGupta, Disha uhttp://www.ncbi.nlm.nih.gov/pubmed/1996510402228nas a2200289 4500008004100000022001400041245012100055210006900176260001200245300001100257490000900268520131200277653001501589653001401604653003301618653002701651653001301678653002901691653001701720653003101737653003201768653002801800100002001828700002301848700001901871856004801890 2008 eng d a1557-170X00aElectrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.0 aElectrocorticographic interictal spike removal via denoising sou c08/2008 a5224-70 v20083 aElectrocorticographic (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.
10aAlgorithms10aArtifacts10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aEvoked Potentials, Motor10aMotor Cortex10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aGunduz, Aysegul1 aSanchez, Justin, C1 aPrincipe, Jose uhttp://www.ncbi.nlm.nih.gov/pubmed/1916389503145nas a2200373 4500008004100000022001400041245005700055210005400112260001200166300001000178490000700188520212100195653001002316653001502326653001802341653002102359653003302380653002702413653001302440653002202453653001102475653001102486653000902497653003502506653003102541653003202572100001902604700001902623700001902642700001702661700002402678700002102702856004802723 2008 eng d a1095-957200aReal-time detection of event-related brain activity.0 aRealtime detection of eventrelated brain activity c11/2008 a245-90 v433 aThe 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.
10aAdult10aAlgorithms10aBrain Mapping10aComputer Systems10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aEvoked Potentials10aFemale10aHumans10aMale10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity1 aSchalk, Gerwin1 aLeuthardt, E C1 aBrunner, Peter1 aOjemann, J G1 aGerhardt, Lester, A1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1871854402201nas a2200289 4500008004100000022001400041245012000055210006900175260000900244300001100253490001200264520126500276653001501541653002801556653003301584653002701617653001301644653001101657653003501668653003301703653003101736653003201767100002601799700002101825700001701846856004801863 2007 eng d a1557-170X00aSpace-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity.0 aSpacetime ICA versus Ensemble ICA for ictal EEG analysis with co c2007 a5473-60 v08/20073 aIn this proof-of-principle study we analyzed intracranial electroencephalogram recordings in patients with intractable focal epilepsy. We contrast two implementations of Independent Component Analysis (ICA) - Ensemble (or spatial) ICA (E-ICA) and Space-Time ICA (ST-ICA) in separating out the ictal components underlying the measurements. In each case we assess the outputs of the ICA algorithms by means of a non-linear method known as the Lempel-Ziv (LZ) complexity. LZ complexity quantifies the complexity of a time series and is well suited to the analysis of non-stationary biomedical signals of short length. Our results show that for small numbers of intracranial recordings, standard E-ICA results in marginal improvements in the separation as measured by the LZ complexity changes. ST-ICA using just 2 recording channels both near and far from the epileptic focus result in more distinct ictal components--although at this stage there is a subjective element to the separation process for ST-ICA. Our results are promising showing that it is possible to extract meaningful information from just 2 recording electrodes through ST-ICA, even if they are not directly over the seizure focus. This work is being further expanded for seizure onset analysis.10aAlgorithms10aArtificial Intelligence10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aHumans10aPattern Recognition, Automated10aPrincipal Component Analysis10aReproducibility of Results10aSensitivity and Specificity1 aJames, Christopher, J1 aAbásolo, Daniel1 aGupta, Disha uhttp://www.ncbi.nlm.nih.gov/pubmed/1800325004762nas a2200325 4500008004100000022001400041245009800055210006900153260001200222300000900234490000600243520379900249653001504048653002704063653002904090653001104119653001604130653001704146653001304163653003504176653003104211653003204242653002804274100001804302700002004320700001204340700001804352700001804370856004804388 2006 eng d a1741-256000aMulti-channel linear descriptors for event-related EEG collected in brain computer interface.0 aMultichannel linear descriptors for eventrelated EEG collected i c03/2006 a52-80 v33 aBy three multi-channel linear descriptors, i.e. spatial complexity (omega), field power (sigma) and frequency of field changes (phi), event-related EEG data within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the event-related EEG data 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-related EEG. 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.
10aAlgorithms10aElectroencephalography10aEvoked Potentials, Motor10aHumans10aImagination10aMotor Cortex10aMovement10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aPei, Xiao-Mei1 aZheng, Shi Dong1 aXu, Jin1 aBin, Guang-yu1 aWang, Zuoguan uhttp://www.ncbi.nlm.nih.gov/pubmed/1651094202760nas a2200433 4500008004100000022001400041245011000055210006900165260001200234300001200246490000700258520140000265653001001665653001501675653003401690653002801724653001001752653001401762653002301776653002701799653002201826653001101848653003101859653003201890653002801922100002401950700002601974700001902000700002402019700001902043700002102062700002002083700002002103700002402123700002002147700002302167700002102190856011502211 2004 eng d a0018-929400aThe BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials.0 aBCI Competition 2003 Progress and perspectives in detection and c06/2004 a1044-510 v513 aInterest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.10aAdult10aAlgorithms10aAmyotrophic Lateral Sclerosis10aArtificial Intelligence10aBrain10aCognition10aDatabases, Factual10aElectroencephalography10aEvoked Potentials10aHumans10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aCurio, Gabriel1 aVaughan, Theresa, M1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aNeuper, Christa1 aPfurtscheller, Gert1 aHinterberger, T1 aSchröder, Michael1 aBirbaumer, Niels uhttps://www.neurotechcenter.org/publications/2004/bci-competition-2003-progress-and-perspectives-detection-and00559nas a2200157 4500008004100000022001400041245010000055210006900155260001200224300002900236490000700265653003100272100002600303700002400329856004800353 2002 eng d a1059-088900aFactor analysis in CAPD and the "unimodal" test battery: do we have a model that will satisfy?.0 aFactor analysis in CAPD and the unimodal test battery do we have c06/2002 a7–9; author reply 9-120 v1110aReproducibility of Results1 aMcFarland, Dennis, J.1 aCacace, Anthony, T. uhttp://www.ncbi.nlm.nih.gov/pubmed/12227358