02632nas a2200217   4500008004100000245012200041210006900163260001200232520184800244653004002092653003502132653004402167653001702211100002102228700002102249700001702270700002102287700002102308700002102329856006402350       2019                        eng d00aAn exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data0 aexploration of BCI performance variations in people with amyotro  c05/20193 aObjective. Brain-computer interface (BCI) technology enables people to use direct measures of brain activity for communication and control. The National Center for Adaptive Neurotechnologies (NCAN) and Helen Hayes Hospital are studying long-term independent home use of P300-based BCIs by people with amyotrophic lateral sclerosis (ALS). This BCI use takes place without technical oversight, and users can encounter substantial variation in their day-to-day BCI performance. The purpose of this study is to identify and evaluate features in the electroencephalogram (EEG) that correlate with successful BCI performance during home use with the goal of improving BCI for people with neuromuscular disorders. Approach. Nine people with ALS used a P300-based BCI at home over several months for communication and computer control. Sessions from a routine calibration task were categorized as successful (≥70%) or unsuccessful (<70%) BCI performance. The correlation of temporal and spectral EEG features with BCI performance was then evaluated. Main Results. BCI performance was positively correlated with an increase in alpha-band (8-14 Hz) activity at locations PO8, P3, Pz, and P4; and beta-band (15-30 Hz) activity at occipital locations. In addition, performance was significantly positively correlated with a positive deflection in EEG amplitude around 220 ms at frontal mid-line locations (i.e., Fz and Cz). BCI performance was negatively correlated with delta-band (1-3 Hz) activity recorded from occipital locations. Significance. These results highlight the variability found in the EEG and describe EEG features that correlate with successful BCI performance during day-to-day use of a P300-based BCI by people with ALS. These results should inform studies focused on improved BCI reliability for people with neuromuscular disorders.10aamyotrophic lateral sclerosis (ALS)10aBrain-computer interface (BCI)10aLongitudinal Electroencephalogram (EEG)10aP300 speller1 aShahriari, Yalda1 aVaughan, Theresa1 aMcCane, Lynn1 aAllison, Brendan1 aWolpaw, Jonathan1 aKrusienski, Dean  uhttps://iopscience.iop.org/article/10.1088/1741-2552/ab22ea02118nas a2200217   4500008004100000245008100041210006900122520141400191653002301605653003501628653001901663653003201682100001701714700001901731700002001750700001501770700001801785700002001803700001901823856005801842       2015                        eng d00aIdentifying the Attended Speaker Using Electrocorticographic (ECoG) Signals.0 aIdentifying the Attended Speaker Using Electrocorticographic ECo3 aPeople affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control. Thus, they cannot use traditional assistive communication devices that depend on muscle control, or brain-computer interfaces (BCIs) that depend on the ability to control gaze. While auditory and tactile BCIs can provide communication to such individuals, their use typically entails an artificial mapping between the stimulus and the communication intent. This makes these BCIs difficult to learn and use.

In this study, we investigated the use of selective auditory attention to natural speech as an avenue for BCI communication. In this approach, the user communicates by directing his/her attention to one of two simultaneously presented speakers. We used electrocorticographic (ECoG) signals in the gamma band (70–170 Hz) to infer the identity of attended speaker, thereby removing the need to learn such an artificial mapping.

Our results from twelve human subjects show that a single cortical location over superior temporal gyrus or pre-motor cortex is typically sufficient to identify the attended speaker within 10 s and with 77% accuracy (50% accuracy due to chance). These results lay the groundwork for future studies that may determine the real-time performance of BCIs based on selective auditory attention to speech.10aauditory attention10aBrain-computer interface (BCI)10aCocktail Party10aelectrocorticography (ECoG)1 aDijkstra, K.1 aBrunner, Peter1 aGunduz, Aysegul1 aCoon, W.G.1 aRitaccio, A L1 aFarquhar, Jason1 aSchalk, Gerwin  uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4776341/03835nas a2200301   4500008004100000022001400041245015300055210006900208260001200277520274700289653005303036653004003089653003503129653003403164653003303198653003503231100002003266700002203286700002603308700002103334700001903355700002103374700001903395700002603414700002103440700002403461856004803485       2015                        eng d  a1872-895200aP300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls.0 aP300based braincomputer interface BCI eventrelated potentials ER  c02/20153 a<p><b>OBJECTIVE: </b>Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities.</p><p><b>METHODS: </b>Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6×6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects.</p><p><b>RESULTS: </b>BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN).</p><p><b>CONCLUSIONS: </b>The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate).</p><p><b>SIGNIFICANCE: </b>P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.</p>10aalternative and augmentative communication (AAC)10aamyotrophic lateral sclerosis (ALS)10aBrain-computer interface (BCI)10abrain-machine interface (BMI)10aelectroencephalography (EEG)10aevent-related potentials (ERP)1 aMcCane, Lynn, M1 aHeckman, Susan, M1 aMcFarland, Dennis, J.1 aTownsend, George1 aMak, Joseph, N1 aSellers, Eric, W1 aZeitlin, Debra1 aTenteromano, Laura, M1 aWolpaw, Jonathan1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/2570394001282nas a2200181   4500008004100000022001400041245005500055210004900110260001200159300001200171520073200183653003500915100002000950700002200970700001900992700001801011856007101029       2015                        eng d  a0018-921900aThe Plurality of Human Brain-Computer Interfacing.0 aPlurality of Human BrainComputer Interfacing  c06/2015  a868-8703 aThe articles in this special issue focus on brain-computer interfacing. The papers are dedicated to this growing and diversifying research enterprise, and features important review articles as well as some important current examples of research in this area. The field of brain-computer interface (BCI) research began to develop about 25 years ago and transformed from initially isolated demonstrations by a few groups into a large scientific enterprise that is currently producing hundreds of peer-reviewed articles and several dedicated conferences and workshops each year. This level of productivity is reflective of the large and continually growing enthusiasm by the scientific community, funding agencies, and the public.10aBrain-computer interface (BCI)1 aMueller-Putz, G1 aMillán, José, R1 aSchalk, Gerwin1 aMueller, K.R.  uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=711530202800nas a2200349   4500008004100000245012100041210006900162260001200231520168800243653003501931653002501966653003201991653002102023653004902044653002302093653001502116653001302131100002602144700002002170700002102190700001902211700001902230700001702249700002602266700002102292700002002313700001802333700001902351700001702370700001502387856004802402       2014                        eng d00aReal-Time Functional Mapping with Electrocorticography in Pediatric Epilepsy: Comparison with fMRI and ESM Findings.0 aRealTime Functional Mapping with Electrocorticography in Pediatr  c07/20143 aSIGFRIED (SIGnal modeling For Real-time Identification and Event Detection) software provides real-time functional mapping (RTFM) of eloquent cortex for epilepsy patients preparing to undergo resective surgery. This study presents the first application of paradigms used in functional magnetic resonance (fMRI) and electrical cortical stimulation mapping (ESM) studies for shared functional cortical mapping in the context of RTFM. Results from the 3 modalities are compared. A left-handed 13-year-old male with intractable epilepsy participated in functional mapping for localization of eloquent language cortex with fMRI, ESM, and RTFM. For RTFM, data were acquired over the frontal and temporal cortex. Several paradigms were sequentially presented: passive (listening to stories) and active (picture naming and verb generation). For verb generation and story processing, fMRI showed atypical right lateralizing language activation within temporal lobe regions of interest and bilateral frontal activation with slight right lateralization. Left hemisphere ESM demonstrated no eloquent language areas. RTFM procedures using story processing and picture naming elicited activity in the right lateral and basal temporal regions. Verb generation elicited strong right lateral temporal lobe activation, as well as left frontal lobe activation. RTFM results confirmed atypical language lateralization evident from fMRI and ESM. We demonstrated the feasibility and usefulness of a new RTFM stimulation paradigm during presurgical evaluation. Block design paradigms used in fMRI may be optimal for this purpose. Further development is needed to create age-appropriate RTFM test batteries.10aBrain-computer interface (BCI)10acortical stimulation10aelectrocorticography (ECoG)10aepilepsy surgery10afunctional magnetic resonance imaging (fMRI)10afunctional mapping10apediatrics10aSIGFRIED1 aKorostenskaja, Milena1 aWilson, Adam, J1 aRose, Douglas, F1 aBrunner, Peter1 aSchalk, Gerwin1 aLeach, James1 aMangano, Francesco, T1 aFujiwara, Hisako1 aRozhkov, Leonid1 aHarris, Elana1 aChen, Po-Ching1 aSeo, Joo-Hee1 aLee, Ki, H  uhttp://www.ncbi.nlm.nih.gov/pubmed/2429316102258nas a2200193   4500008004100000022001400041245006700055210006500122260001200187300001100199490000600210520166100216653003501877653003401912653003201946100001901978700001901997856004802016       2011                        eng d  a1941-118900aBrain-computer interfaces using electrocorticographic signals.0 aBraincomputer interfaces using electrocorticographic signals  c10/2011  a140-540 v43 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Many studies over the past two decades have shown that people and animals can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems measure specific features of brain activity and translate them into control signals that drive an output. The sensor modalities that have most commonly been used in BCI studies have been electroencephalographic (EEG) recordings from the scalp and single-neuron recordings from within the cortex. Over the past decade, an increasing number of studies has explored the use of electrocorticographic (ECoG) activity recorded directly from the surface of the brain. ECoG has attracted substantial and increasing interest, because it has been shown to reflect specific details of actual and imagined actions, and because its technical characteristics should readily support robust and chronic implementations of BCI systems in humans. This review provides general perspectives on the ECoG platform; describes the different electrophysiological features that can be detected in ECoG; elaborates on the signal acquisition issues, protocols, and online performance of ECoG-based BCI studies to date; presents important limitations of current ECoG studies; discusses opportunities for further research; and finally presents a vision for eventual clinical implementation. In summary, the studies presented to date strongly encourage further research using the ECoG platform for basic neuroscientific research, as well as for translational neuroprosthetic applications.</span></p>10aBrain-computer interface (BCI)10abrain-machine interface (BMI)10aelectrocorticography (ECoG)1 aSchalk, Gerwin1 aLeuthardt, E C  uhttp://www.ncbi.nlm.nih.gov/pubmed/2227379601200nas a2200229   4500008004100000022001400041245009400055210007100149260001200220300001400232490000700246520047400253653003500727653001500762653002200777100002600799700002600825700002600851700002000877700002500897856004800922       2006                        eng d  a1534-432000aBCI Meeting 2005–workshop on BCI signal processing: feature extraction and translation.0 aBCI Meeting 2005–workshop on BCI signal processing feature extra  c06/2006  a135–1380 v143 aThis paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.10aBrain-computer interface (BCI)10aprediction10aSignal Processing1 aMcFarland, Dennis, J.1 aAnderson, Charles, W.1 aMüller, Klaus-Robert1 aSchlögl, Alois1 aKrusienski, Dean, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679227801530nas a2200277   4500008004100000022001400041245006600055210006500121260001200186300001400198490000700212520068900219653003500908653003300943653001900976100002100995700002101016700002501037700002101062700003001083700002701113700001901140700002301159700002201182856004801204       2006                        eng d  a1534-432000aBCI Meeting 2005–workshop on signals and recording methods.0 aBCI Meeting 2005–workshop on signals and recording methods  c06/2006  a138–1410 v143 aThis paper describes the highlights of presentations and discussions during the Third International BCI Meeting in a workshop that evaluated potential brain-computer interface (BCI) signals and currently available recording methods. It defined the main potential user populations and their needs, addressed the relative advantages and disadvantages of noninvasive and implanted (i.e., invasive) methodologies, considered ethical issues, and focused on the challenges involved in translating BCI systems from the laboratory to widespread clinical use. The workshop stressed the critical importance of developing useful applications that establish the practical value of BCI technology.10aBrain-computer interface (BCI)10aelectrophysiological signals10aRehabilitation1 aWolpaw, Jonathan1 aLoeb, Gerald, E.1 aAllison, Brendan, Z.1 aDonchin, Emanuel1 aNascimento, Omar, Feix do1 aHeetderks, William, J.1 aNijboer, Femke1 aShain, William, G.1 aTurner, James, N.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679227902353nas a2200229   4500008004100000022001400041245008900055210006900144260001200213300001400225490000700239520162900246653003101875653003501906653002701941653001301968100002301981700002602004700002102030700002402051856004802075       2004                        eng d  a1534-432000aConversion of EEG activity into cursor movement by a brain-computer interface (BCI).0 aConversion of EEG activity into cursor movement by a braincomput  c09/2004  a331–3380 v123 aThe 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.10aaugmentative communication10aBrain-computer interface (BCI)10aElectroencephalography10aFeedback1 aFabiani, Georg, E.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan1 aPfurtscheller, Gert  uhttp://www.ncbi.nlm.nih.gov/pubmed/1547319502907nas a2200325   4500008004100000022001400041245008700055210006900142260001200211300001300223490000700236520189600243653003102139653003502170653003302205653001902238100002402257700002702281700002302308700002302331700002202354700002202376700002002398700002202418700002102440700002102461700003002482700002102512856004802533       2003                        eng d  a1534-432000aBrain-computer interface technology: a review of the Second International Meeting.0 aBraincomputer interface technology a review of the Second Intern  c06/2003  a94–1090 v113 aThis paper summarizes the Brain-Computer Interfaces for Communication and Control, The Second International Meeting, held in Rensselaerville, NY, in June 2002. Sponsored by the National Institutes of Health and organized by the Wadsworth Center of the New York State Department of Health, the meeting addressed current work and future plans in brain-computer interface (BCI) research. Ninety-two researchers representing 38 different research groups from the United States, Canada, Europe, and China participated. The BCIs discussed at the meeting use electroencephalographic activity recorded from the scalp or single-neuron activity recorded within cortex to control cursor movement, select letters or icons, or operate neuroprostheses. 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 that recognizes the commands contained in the input and expresses them in device control. Current BCIs have maximum information transfer rates of up to 25 b/min. Achievement of greater speed and accuracy requires improvements in signal acquisition and processing, in translation algorithms, and in user training. These improvements depend on interdisciplinary cooperation among neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective criteria for evaluating alternative methods. The practical use of BCI technology will be determined by the development of appropriate applications and identification of appropriate user groups, and will require careful attention to the needs and desires of individual users.10aaugmentative communication10aBrain-computer interface (BCI)10aelectroencephalography (EEG)10aRehabilitation1 aVaughan, Theresa, M1 aHeetderks, William, J.1 aTrejo, Leonard, J.1 aRymer, William, Z.1 aWeinrich, Michael1 aMoore, Melody, M.1 aKübler, Andrea1 aDobkin, Bruce, H.1 aBirbaumer, Niels1 aDonchin, Emanuel1 aWolpaw, Elizabeth, Winter1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1289924702866nas a2200289   4500008004100000022001400041245008600055210006900141260001200210300001400222490000600236520197100242653003102213653003502244653003302279100002102312700002102333700002202354700002602376700002002402700001902422700002102441700002102462700002102483700002402504856004802528       2000                        eng d  a1063-652800aBrain-computer interface technology: a review of the first international meeting.0 aBraincomputer interface technology a review of the first interna  c06/2000  a164–1730 v83 aOver the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.10aaugmentative communication10aBrain-computer interface (BCI)10aelectroencephalography (EEG)1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aHeetderks, W., J.1 aMcFarland, Dennis, J.1 aPeckham, P., H.1 aSchalk, Gerwin1 aDonchin, Emanuel1 aQuatrano, L., A.1 aRobinson, C., J.1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/10896178