03178nas a2200421   4500008004100000022001400041245009800055210006900153260001200222300001100234490000700245520196800252653001002220653000902230653001502239653002402254653003002278653003602308653002702344653002102371653003102392653001102423653001102434653000902445653002402454653001602478653001702494653002202511653002802533100002502561700001602586700001902602700002002621700002202641700002102663700002402684856004802708       2014                        eng d  a1741-255200aA practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening.0 apractical intuitive braincomputer interface for communicating ye  c06/2014  a0350030 v113 aOBJECTIVE:
Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words 'yes' and 'no') without loss of performance, and whether the system could be used by people in the locked-in state.
APPROACH:
We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions.
MAIN RESULTS:
The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects' performance with word stimuli (mean 77% ± 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73% ± 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects.
SIGNIFICANCE:
Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in.10aAdult10aAged10aAlgorithms10aAuditory Perception10abrain-computer interfaces10aCommunication Aids for Disabled10aElectroencephalography10aEquipment Design10aEquipment Failure Analysis10aFemale10aHumans10aMale10aMan-Machine Systems10aMiddle Aged10aQuadriplegia10aTreatment Outcome10aUser-Computer Interface1 aHill, Jeremy, Jeremy1 aRicci, Erin1 aHaider, Sameah1 aMcCane, Lynn, M1 aHeckman, Susan, M1 aWolpaw, Jonathan1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/2483827802228nas a2200181   4500008004100000022001400041245004300055210004100098260001200139300001400151490000700165520173200172653002801904100002001932700002501952700002101977856004801998       2012                        eng d  a1942-554600aBrain-computer interfaces in medicine.0 aBraincomputer interfaces in medicine  c03/2012  a268–2790 v873 aBrain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function.10aUser-Computer Interface1 aShih, Jerry, J.1 aKrusienski, Dean, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2232536402062nas a2200241   4500008004100000022001400041245012200055210006900177260001200246300001100258490000600269520128800275653002801563100002001591700002601611700002401637700002101661700002401682700002301706700002201729700002101751856004801772       2012                        eng d  a1741-255200aEEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis.0 aEEG correlates of P300based braincomputer interface BCI performa  c04/2012  a0260140 v93 aThe purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for &gt;60% of the variance in copy-spelling performance (p &lt; 0.001, mean R(2)?= 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.10aUser-Computer Interface1 aMak, Joseph, N.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aMcCane, Lynn, M.1 aTsui, Phillippa, Z.1 aZeitlin, Debra, J.1 aSellers, Eric, W.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2235050102099nas a2200241   4500008004100000022001400041245011200055210006900167260001200236300001000248490000700258520136500265653001501630653001001645653002701655653001101682653001701693653002801710100002401738700002601762700002101788856004801809       2012                        eng d  a1873-274700aValue of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface.0 aValue of amplitude phase and coherence features for a sensorimot  c01/2012  a130-40 v873 aMeasures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined phase-locking value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of inter-channel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source.10aAlgorithms10aBrain10aElectroencephalography10aHumans10aMotor Cortex10aUser-Computer Interface1 aKrusienski, Dean, J1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2198598400539nas a2200157   4500008004100000022001400041245010700055210006900162260001200231300001100243490000600254653002800260100002400288700002100312856004800333       2011                        eng d  a1741-255200aSpecial issue containing contributions from the Fourth International Brain-Computer Interface Meeting.0 aSpecial issue containing contributions from the Fourth Internati  c04/2011  a0202010 v810aUser-Computer Interface1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2143652201801nas a2200181   4500008004100000022001400041245006700055210006200122260001200184300001400196490000700210520125900217653002801476100002201504700002401526700002101550856004801571       2010                        eng d  a1471-180X00aA brain-computer interface for long-term independent home use.0 abraincomputer interface for longterm independent home use  c10/2010  a449–4550 v113 aOur objective was to develop and validate a new brain-computer interface (BCI) system suitable for long-term independent home use by people with severe motor disabilities. The BCI was used by a 51-year-old male with ALS who could no longer use conventional assistive devices. Caregivers learned to place the electrode cap, add electrode gel, and turn on the BCI. After calibration, the system allowed the user to communicate via EEG. Re-calibration was performed remotely (via the internet), and BCI accuracy assessed in periodic tests. Reports of BCI usefulness by the user and the family were also recorded. Results showed that BCI accuracy remained at 83% (r = -.07, n.s.) for over 2.5 years (1.4% expected by chance). The BCI user and his family state that the BCI had restored his independence in social interactions and at work. He uses the BCI to run his NIH-funded research laboratory and to communicate via e-mail with family, friends, and colleagues. In addition to this first user, several other similarly disabled people are now using the BCI in their daily lives. In conclusion, long-term independent home use of this BCI system is practical for severely disabled people, and can contribute significantly to quality of life and productivity.10aUser-Computer Interface1 aSellers, Eric, W.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2058394702830nas a2200337   4500008004100000022001400041245004900055210004500104260001200149300001100161490000600172520196200178653001002140653003502150653001802185653001102203653001102214653000902225653001602234653002502250653002302275653002802298653001602326100001902342700001302361700001502374700002102389700001502410700001902425856004802444       2010                        eng d  a1741-255200aDoes the 'P300' speller depend on eye gaze?.0 aDoes the P300 speller depend on eye gaze  c10/2010  a0560130 v73 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain-computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called 'P300 matrix speller' that was first described by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). It has been widely assumed that this method does not depend on the ability to focus on the desired character, because it was thought that it relies primarily on the P300-evoked potential and minimally, if at all, on other EEG features such as the visual-evoked potential (VEP). This issue is highly relevant for the clinical application of this BCI method, because eye movements may be impaired or lost in the relevant user population. This study investigated the extent to which the performance in a 'P300' speller BCI depends on eye gaze. We evaluated the performance of 17 healthy subjects using a 'P300' matrix speller under two conditions. Under one condition ('letter'), the subjects focused their eye gaze on the intended letter, while under the second condition ('center'), the subjects focused their eye gaze on a fixation cross that was located in the center of the matrix. The results show that the performance of the 'P300' matrix speller in normal subjects depends in considerable measure on gaze direction. They thereby disprove a widespread assumption in BCI research, and suggest that this BCI might function more effectively for people who retain some eye-movement control. The applicability of these findings to people with severe neuromuscular disabilities (particularly in eye-movements) remains to be determined.</span></p>10aAdult10aEvent-Related Potentials, P30010aEye Movements10aFemale10aHumans10aMale10aMiddle Aged10aModels, Neurological10aPhotic Stimulation10aUser-Computer Interface10aYoung Adult1 aBrunner, Peter1 aJoshi, S1 aBriskin, S1 aWolpaw, Jonathan1 aBischof, H1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2085892401515nas a2200181   4500008004100000022001400041245007300055210006900128260001200197300001100209490000600220520095800226653002801184100002601212700002601238700002101264856004801285       2010                        eng d  a1741-255200aElectroencephalographic (EEG) control of three-dimensional movement.0 aElectroencephalographic EEG control of threedimensional movement  c06/2010  a0360070 v73 aBrain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.10aUser-Computer Interface1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2046069001458nas a2200169   4500008004100000022001400041245011700055210006900172260001200241300001400253490000700267520089200274653002801166100002501194700002101219856004801240       2009                        eng d  a0074-774200aBrain-computer interface research at the wadsworth center developments in noninvasive communication and control.0 aBraincomputer interface research at the wadsworth center develop  c02/2009  a147–1570 v863 aBrain-computer interface (BCI) research at the Wadsworth Center focuses on noninvasive, electroencephalography (EEG)-based BCI methods for helping severely disabled individuals communicate and interact with their environment. We have demonstrated that these individuals, as well as able-bodied individuals, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one and two dimensions. We have also developed a practical P300-based BCI that enables users to access and control the full functionality of their personal computer. We are currently translating this laboratory-proved BCI technology into a system that can be used by severely disabled individuals in their homes with minimal ongoing technical oversight. Our comprehensive approach to BCI design has led to several innovations that are applicable in other BCI contexts, such as space missions.10aUser-Computer Interface1 aKrusienski, Dean, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1960799705137nas a2200373   4500008004100000022001400041245007500055210006900130260001200199300001000211490000800221520406800229653001004297653001504307653001004322653001804332653002404350653002704374653001104401653000904412653002404421653002404445653001904469653003604488653004104524653002404565653002804589100001904617700001904636700002404655700001504679700002104694856004804715       2008                        eng d  a0165-027000aBrain-computer interfaces (BCIs): Detection Instead of Classification.0 aBraincomputer interfaces BCIs Detection Instead of Classificatio  c01/2008  a51-620 v1673 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Many studies over the past two decades have shown that people can use&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals to convey their intent to a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;through&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs). These devices operate by recording signals from the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;applications is the current requirement for preliminary analyses to identify the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;activity so that any change in&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">relevant</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.</span></p>10aAdult10aAlgorithms10aBrain10aBrain Mapping10aElectrocardiography10aElectroencephalography10aHumans10aMale10aMan-Machine Systems10aNormal Distribution10aOnline Systems10aSignal Detection, Psychological10aSignal Processing, Computer-Assisted10aSoftware Validation10aUser-Computer Interface1 aSchalk, Gerwin1 aBrunner, Peter1 aGerhardt, Lester, A1 aBischof, H1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1792013401806nas a2200169   4500008004100000022001400041245006200055210006000117260001200177300001600189490000600205520130800211653002801519100002001547700002101567856004801588       2008                        eng d  a1474-442200aBrain-computer interfaces in neurological rehabilitation.0 aBraincomputer interfaces in neurological rehabilitation  c11/2008  a1032–10430 v73 aRecent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.10aUser-Computer Interface1 aDaly, Janis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1883554101945nas a2200193   4500008004100000022001400041245008500055210006900140260001200209300001400221490000600235520133600241653002801577100002601605700002501631700002601656700002101682856004801703       2008                        eng d  a1741-256000aEmulation of computer mouse control with a noninvasive brain-computer interface.0 aEmulation of computer mouse control with a noninvasive braincomp  c06/2008  a101–1100 v53 aBrain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user's task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.10aUser-Computer Interface1 aMcFarland, Dennis, J.1 aKrusienski, Dean, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1836777901908nas a2200169   4500008004100000022001400041245012200055210006900177260001200246300001400258490000600272520133700278653002801615100002601643700002101669856004801690       2008                        eng d  a1741-256000aSensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis.0 aSensorimotor rhythmbased braincomputer interface BCI model order  c06/2008  a155–1620 v53 aPeople can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoregressive model has varied widely among studies. Through analyses of both simulated and actual EEG data, the present study examines the effects of model order on sensorimotor rhythm measurements and BCI performance. The results show that resolution of lower frequency signals requires higher model orders and that this requirement reflects the temporal span of the model coefficients. This is true for both simulated EEG data and actual EEG data during brain-computer interface (BCI) operation. Increasing model order, and decimating the signal were similarly effective in increasing spectral resolution. Furthermore, for BCI control of two-dimensional cursor movement, higher model orders produced better performance in each dimension and greater independence between horizontal and vertical movements. In sum, these results show that autoregressive model order selection is an important determinant of BCI performance and should be based on criteria that reflect system performance.10aUser-Computer Interface1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1843097404357nas a2200385   4500008004100000022001400041245009700055210006900152260001200221300001200233490000800245520323100253653001503484653001003499653001403509653001003523653001803533653004203551653002703593653003003620653001103650653001103661653000903672653003203681653002303713653002203736653002803758100002503786700002603811700001903837700002003856700002603876700002103902856004803923       2008                        eng d  a1388-245700aTowards an independent brain-computer interface using steady state visual evoked potentials.0 aTowards an independent braincomputer interface using steady stat  c02/2008  a399-4080 v1193 a<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSIONS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The&nbsp;<span class="highlight">data</span>&nbsp;demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">SIGNIFICANCE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.</p>10aAdolescent10aAdult10aAttention10aBrain10aBrain Mapping10aDose-Response Relationship, Radiation10aElectroencephalography10aEvoked Potentials, Visual10aFemale10aHumans10aMale10aPattern Recognition, Visual10aPhotic Stimulation10aSpectrum Analysis10aUser-Computer Interface1 aAllison, Brendan, Z.1 aMcFarland, Dennis, J.1 aSchalk, Gerwin1 aZheng, Shi Dong1 aMoore-Jackson, Melody1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1807720802662nas a2200409   4500008004100000022001400041245008400055210006900139260001200208300001000220490000600230520153900236653001501775653001001790653001801800653003701818653002001855653002401875653002601899653002701925653001301952653001101965653001101976653000901987653001301996653002802009100001902037700001602056700002602072700002002098700001602118700001702134700001302151700002102164700001902185856004802204       2008                        eng d  a1741-256000aTwo-dimensional movement control using electrocorticographic signals in humans.0 aTwodimensional movement control using electrocorticographic sign  c03/2008  a75-840 v53 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We show here that a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73% in a two-dimensional four-target center-out task in which chance accuracy was 25%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;practicality, and also indicate promising directions for further research.</span></p>10aAdolescent10aAdult10aBrain Mapping10aData Interpretation, Statistical10aDrug Resistance10aElectrocardiography10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aHumans10aMale10aMovement10aUser-Computer Interface1 aSchalk, Gerwin1 aMiller, K J1 aAnderson, Nicholas, R1 aWilson, Adam, J1 aSmyth, Matt1 aOjemann, J G1 aMoran, D1 aWolpaw, Jonathan1 aLeuthardt, E C  uhttp://www.ncbi.nlm.nih.gov/pubmed/1831081302957nas a2200157   4500008004100000022001400041245006000055210005800115260001200173300001400185490000800199520249500207653002802702100002102730856004802751       2007                        eng d  a0022-375100aBrain-computer interfaces as new brain output pathways.0 aBraincomputer interfaces as new brain output pathways  c03/2007  a613–6190 v5793 aBrain-computer interfaces (BCIs) can provide non-muscular communication and control for people with severe motor disabilities. Current BCIs use a variety of invasive and non-invasive methods to record brain signals and a variety of signal processing methods. Whatever the recording and processing methods used, BCI performance (e.g. the ability of a BCI to control movement of a computer cursor) is highly variable and, by the standards applied to neuromuscular control, could be described as ataxic. In an effort to understand this imperfection, this paper discusses the relevance of two principles that underlie the brain's normal motor outputs. The first principle is that motor outputs are normally produced by the combined activity of many CNS areas, from the cortex to the spinal cord. Together, these areas produce appropriate control of the spinal motoneurons that activate muscles. The second principle is that the acquisition and life-long preservation of motor skills depends on continual adaptive plasticity throughout the CNS. This plasticity optimizes the control of spinal motoneurons. In the light of these two principles, a BCI may be viewed as a system that changes the outcome of CNS activity from control of spinal motoneurons to, instead, control of the cortical (or other) area whose signals are used by the BCI to determine the user's intent. In essence, a BCI attempts to assign to cortical neurons the role normally performed by spinal motoneurons. Thus, a BCI requires that the many CNS areas involved in producing normal motor actions change their roles so as to optimize the control of cortical neurons rather than spinal motoneurons. The disconcerting variability of BCI performance may stem in large part from the challenge presented by the need for this unnatural adaptation. This difficulty might be reduced, and BCI development might thereby benefit, by adopting a 'goal-selection' rather than a 'process- control' strategy. In 'process control', a BCI manages all the intricate high-speed interactions involved in movement. In 'goal selection', by contrast, the BCI simply communicates the user's goal to software that handles the high-speed interactions needed to achieve the goal. Not only is 'goal selection' less demanding, but also, by delegating lower-level aspects of motor control to another structure (rather than requiring that the cortex do everything), it more closely resembles the distributed operation characteristic of normal motor control.10aUser-Computer Interface1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1725516402623nas a2200289   4500008004100000022001400041245008600055210007000141260001200211300001100223490000700234520175100241653001501992653002002007653002902027653002702056653002202083653001102105653001602116653003502132653002802167100002402195700001902219700002602238700002102264856004802285       2007                        eng d  a0018-929400aA µ-rhythm Matched Filter for  Continuous Control of a Brain-Computer Interface.0 aµrhythm Matched Filter for Continuous Control of a BrainComputer  c02/2007  a273-800 v543 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">continuous</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">amplitude fluctuations fail to capture essential amplitude/phase relationships of the mu and beta rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic mu rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic mu rhythm is proposed and its effectiveness as a matched filter is examined online for a one-dimensional cursor control task. The results suggest that amplitude/phase coupling exists between the mu and beta bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance.</span></p>10aAlgorithms10aCerebral Cortex10aCortical Synchronization10aElectroencephalography10aEvoked Potentials10aHumans10aImagination10aPattern Recognition, Automated10aUser-Computer Interface1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1727858403802nas a2200385   4500008004100000022001400041245008700055210006900142260001200211300001000223490000700233520264300240653001502883653001002898653003602908653002302944653002702967653002202994653001103016653002703027653002403054653003803078653002803116100002403144700002603168700002403194700001903218700002103237700002003258700002403278700002203302700002303324700002103347856004803368       2006                        eng d  a1534-432000aThe BCI competition III: Validating alternative approaches to actual BCI problems.0 aBCI competition III Validating alternative approaches to actual   c06/2006  a153-90 v143 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">A&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCI) is a system that allows its users to control external devices with&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, which produces&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">online</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.</span></p>10aAlgorithms10aBrain10aCommunication Aids for Disabled10aDatabases, Factual10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aSoftware Validation10aTechnology Assessment, Biomedical10aUser-Computer Interface1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aPfurtscheller, Gert1 aMillán, José, R1 aSchröder, Michael1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679228201404nas a2200181   4500008004100000022001400041245011100055210006900166260001200235300001600247490000600263520080700269653002801076100002301104700002601127700002101153856004801174       2006                        eng d  a1557-170X00aAn evaluation of autoregressive spectral estimation model order for brain-computer interface applications.0 aevaluation of autoregressive spectral estimation model order for  c09/2006  a1323–13260 v13 aAutoregressive (AR) spectral estimation is a popular method for modeling the electroencephalogram (EEG), and therefore the frequency domain EEG phenomena that are used for control of a brain-computer interface (BCI). Several studies have been conducted to evaluate the optimal AR model order for EEG, but the criteria used in these studies does not necessarily equate to the optimal AR model order for sensorimotor rhythm (SMR)-based BCI control applications. The present study confirms this by evaluating the EEG spectra of data obtained during control of SMR-BCI using different AR model orders and model evaluation criteria. The results indicate that the AR model order that optimizes SMR-BCI control performance is generally higher than the model orders that are frequently used in SMR-BCI studies.10aUser-Computer Interface1 aKrusienski, D., J.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1794603801922nas a2200169   4500008004100000022001400041245009700055210006900152260001200221300001400233490000700247520137700254653002801631100002401659700002101683856004801704       2006                        eng d  a1534-432000aThe Third International Meeting on Brain-Computer Interface Technology: making a difference.0 aThird International Meeting on BrainComputer Interface Technolog  c06/2006  a126–1270 v143 aThis special issue of the IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING provides a representative and comprehensive bird's-eye view of the most recent developments in brain-computer interface (BCI) technology from laboratories around the world. The 30 research communications and papers are the direct outcome of the Third International Meeting on Brain-Computer Interface Technology held at the Rensselaerville Institute, Rensselaerville, NY, in June 2005. Fifty-three research groups from North and South America, Europe, and Asia, representing the majority of all the existing BCI laboratories around the world, participated in this highly focused meeting sponsored by the National Institutes of Health and organized by the BCI Laboratory of the Wadsworth Center of the New York State Department of Health. As demonstrated by the papers in this special issue, the rapid advances in BCI research and development make this technology capable of providing communication and control to people severely disabled by amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, and other neuromuscular disorders. Future work is expected to improve the performance and utility of BCIs, and to focus increasingly on making them a viable, practical, and affordable communication alternative for many thousands of severely disabled people worldwide.10aUser-Computer Interface1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679227502977nas a2200361   4500008004100000022001400041245007400055210006800129260001200197300001100209490000700220520195300227653001202180653001002192653002702202653002202229653001102251653002702262653001302289653001302302653001602315653003102331653001702362653002802379100002402407700002602431700001902457700002502476700002402501700002102525700002102546856004802567       2006                        eng d  a1534-432000aThe Wadsworth BCI Research and Development Program: At Home with BCI.0 aWadsworth BCI Research and Development Program At Home with BCI  c06/2006  a229-330 v143 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive,&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">electroencephalography</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">complexity</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.</span></p>10aAnimals10aBrain10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aNew York10aResearch10aSwitzerland10aTherapy, Computer-Assisted10aUniversities10aUser-Computer Interface1 aVaughan, Theresa, M1 aMcFarland, Dennis, J.1 aSchalk, Gerwin1 aSarnacki, William, A1 aKrusienski, Dean, J1 aSellers, Eric, W1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679230102332nas a2200457   4500008004100000022001400041245009000055210006900145260001200214300001100226490000700237520103300244653000901277653003401286653002701320653002901347653003701376653001101413653001101424653001601435653000901451653001601460653001701476653001301493653001401506653002301520653002801543653002501571653002201596653002801618100001501646700001501661700002301676700002401699700001601723700001901739700002601758700002101784700002101805856004801826       2005                        eng d  a1526-632X00aPatients with ALS can use sensorimotor rhythms to operate a brain-computer interface.0 aPatients with ALS can use sensorimotor rhythms to operate a brai  c05/2005  a1775-70 v643 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">People with severe motor disabilities can maintain an acceptable quality of life if they can communicate.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">based</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;BCI could help maintain quality of life for people with ALS.</span></p>10aAged10aAmyotrophic Lateral Sclerosis10aElectroencephalography10aEvoked Potentials, Motor10aEvoked Potentials, Somatosensory10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aMotor Cortex10aMovement10aParalysis10aPhotic Stimulation10aProstheses and Implants10aSomatosensory Cortex10aTreatment Outcome10aUser-Computer Interface1 aKübler, A1 aNijboer, F1 aMellinger, Jürgen1 aVaughan, Theresa, M1 aPawelzik, H1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1591180901195nas a2200253   4500008004100000022001400041245009000055210006900145260001200214300001600226490000700242520044000249653002800689100001500717700001600732700001700748700002400765700001700789700001900806700002600825700002100851700002100872856004800893       2005                        eng d  a1526-632X00aPatients with ALS can use sensorimotor rhythms to operate a brain-computer interface.0 aPatients with ALS can use sensorimotor rhythms to operate a brai  c05/2005  a1775–17770 v643 aPeople 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.10aUser-Computer Interface1 aKübler, A1 aNijboer, F.1 aMellinger, J1 aVaughan, Theresa, M1 aPawelzik, H.1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1591180902760nas a2200433   4500008004100000022001400041245011000055210006900165260001200234300001200246490000700258520140000265653001001665653001501675653003401690653002801724653001001752653001401762653002301776653002701799653002201826653001101848653003101859653003201890653002801922100002401950700002601974700001902000700002402019700001902043700002102062700002002083700002002103700002402123700002002147700002302167700002102190856011502211       2004                        eng d  a0018-929400aThe BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials.0 aBCI Competition 2003 Progress and perspectives in detection and   c06/2004  a1044-510 v513 aInterest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.10aAdult10aAlgorithms10aAmyotrophic Lateral Sclerosis10aArtificial Intelligence10aBrain10aCognition10aDatabases, Factual10aElectroencephalography10aEvoked Potentials10aHumans10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aCurio, Gabriel1 aVaughan, Theresa, M1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aNeuper, Christa1 aPfurtscheller, Gert1 aHinterberger, T1 aSchröder, Michael1 aBirbaumer, Niels  uhttps://www.neurotechcenter.org/publications/2004/bci-competition-2003-progress-and-perspectives-detection-and02705nas a2200337   4500008004100000022001400041245007000055210006400125260001200189300001200201490000700213520166200220653001501882653001001897653001401907653003601921653002501957653002701982653002102009653003102030653002202061653001102083653002402094653002802118100001902146700002602165700002002191700002102211700002102232856011402253       2004                        eng d  a0018-929400aBCI2000: a general-purpose brain-computer interface (BCI) system.0 aBCI2000 a generalpurpose braincomputer interface BCI system  c06/2004  a1034-430 v513 aMany laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.10aAlgorithms10aBrain10aCognition10aCommunication Aids for Disabled10aComputer Peripherals10aElectroencephalography10aEquipment Design10aEquipment Failure Analysis10aEvoked Potentials10aHumans10aSystems Integration10aUser-Computer Interface1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aHinterberger, T1 aBirbaumer, Niels1 aWolpaw, Jonathan  uhttps://www.neurotechcenter.org/publications/2004/bci2000-general-purpose-brain-computer-interface-bci-system02279nas a2200205   4500008004100000022001400041245007000055210006400125260001200189300001600201490000700217520166200224653002801886100001901914700002601933700002401959700002101983700002102004856004802025       2004                        eng d  a0018-929400aBCI2000: a general-purpose brain-computer interface (BCI) system.0 aBCI2000 a generalpurpose braincomputer interface BCI system  c06/2004  a1034–10430 v513 aMany laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.10aUser-Computer Interface1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aHinterberger, Thilo1 aBirbaumer, Niels1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1518887503554nas a2200361   4500008004100000022001400041245007800055210006900133260001200202300001000214490000600224520253800230653001002768653001002778653003602788653002502824653003302849653002602882653002702908653002202935653001102957653001102968653001602979653000902995653002303004653002803027100001903055700001903074700002103093700001703114700001303131856004803144       2004                        eng d  a1741-256000aA brain-computer interface using electrocorticographic signals in humans.0 abraincomputer interface using electrocorticographic signals in h  c06/2004  a63-710 v13 a<p><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;can enable users to control a one-dimensional&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">.</span></p>10aAdult10aBrain10aCommunication Aids for Disabled10aComputer Peripherals10aDiagnosis, Computer-Assisted10aElectrodes, Implanted10aElectroencephalography10aEvoked Potentials10aFemale10aHumans10aImagination10aMale10aMovement Disorders10aUser-Computer Interface1 aLeuthardt, E C1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aOjemann, J G1 aMoran, D  uhttp://www.ncbi.nlm.nih.gov/pubmed/1587662403064nas a2200373   4500008004100000022001400041245009000055210006900145260001200214300001000226490000700236520200500243653002902248653001002277653001502287653001402302653001002316653001802326653002702344653003002371653001302401653001102414653001602425653002802441653001302469653002002482653002802502653002202530100002102552700002602573700002402599700001902623856004802642       2003                        eng d  a1534-432000aThe Wadsworth Center brain-computer interface (BCI) research and development program.0 aWadsworth Center braincomputer interface BCI research and develo  c06/2003  a204-70 v113 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interface (BCI) research at the Wadsworth Center has focused primarily on using electroencephalogram (EEG) rhythms recorded from the scalp over sensorimotor cortex to&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;by improving the selection of signal features and their translation into device commands, by incorporating additional signal features, and by optimizing the adaptive interaction between the user and system. In addition, to facilitate the evaluation, comparison, and combination of alternative BCI methods, we have developed a general-purpose BCI system called BCI-2000 and have made it available to other research&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">groups</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. Finally, in collaboration with several other&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">groups</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.</span></p>10aAcademic Medical Centers10aAdult10aAlgorithms10aArtifacts10aBrain10aBrain Mapping10aElectroencephalography10aEvoked Potentials, Visual10aFeedback10aHumans10aMiddle Aged10aNervous System Diseases10aResearch10aResearch Design10aUser-Computer Interface10aVisual Perception1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/1289927506596nas a2200265   4500008004100000022001400041245006100055210005900116260001200175300001100187490000800198520581800206653001906024653003606043653002106079653002706100653001106127653002806138100002106166700002106187700002606208700002406234700002406258856004806282       2002                        eng d  a1388-245700aBrain-computer interfaces for communication and control.0 aBraincomputer interfaces for communication and control  c06/2002  a767-910 v1133 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10-25bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;option for those with motor disabilities and might also give those without disabilities a supplementary&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;channel or a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;channel useful in special circumstances.</span></p>10aBrain Diseases10aCommunication Aids for Disabled10aComputer Systems10aElectroencephalography10aHumans10aUser-Computer Interface1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aMcFarland, Dennis, J.1 aPfurtscheller, Gert1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/1204803801388nas a2200205   4500008004100000022001400041245005900055210005600114260001200170300001400182490000800196520081100204653002801015100001501043700001901058700001501077700002101092700002101113856004801134       2001                        eng d  a0033-290900aBrain-computer communication: unlocking the locked in.0 aBraincomputer communication unlocking the locked in  c05/2001  a358–3750 v1273 aWith the increasing efficiency of life-support systems and better intensive care, more patients survive severe injuries of the brain and spinal cord. Many of these patients experience locked-in syndrome: The active mind is locked in a paralyzed body. Consequently, communication is extremely restricted or impossible. A muscle-independent communication channel overcomes this problem and is realized through a brain-computer interface, a direct connection between brain and computer. The number of technically elaborated brain-computer interfaces is in contrast with the number of systems used in the daily life of locked-in patients. It is hypothesized that a profound knowledge and consideration of psychological principles are necessary to make brain-computer interfaces feasible for locked-in patients.10aUser-Computer Interface1 aKübler, A1 aKotchoubey, B.1 aKaiser, J.1 aWolpaw, Jonathan1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/1139330101157nas a2200181   4500008004100000022001400041245006300055210006100118260001200179300001400191490000600205520061700211653002800828100002100856700002600877700002400903856004800927       2000                        eng d  a1063-652800aBrain-computer interface research at the Wadsworth Center.0 aBraincomputer interface research at the Wadsworth Center  c06/2000  a222–2260 v83 aStudies at the Wadsworth Center over the past 14 years have shown that people with or without motor disabilities can learn to control the amplitude of mu or beta rhythms in electroencephalographic (EEG) activity recorded from the scalp over sensorimotor cortex and can use that control to move a cursor on a computer screen in one or two dimensions. This EEG-based brain-computer interface (BCI) could provide a new augmentative communication technology for those who are totally paralyzed or have other severe motor impairments. Present research focuses on improving the speed and accuracy of BCI communication.10aUser-Computer Interface1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/1089619403091nas a2200373   4500008004100000022001400041245008600055210006900141260001200210300001100222490000600233520197800239653001502217653002002232653003602252653002102288653002702309653002202336653001102358653002702369653004102396653002802437100002102465700002102486700001902507700002602526700001702552700001902569700002102588700001802609700001802627700002402645856004802669       2000                        eng d  a1063-652800aBrain-computer interface technology: a review of the first international meeting.0 aBraincomputer interface technology a review of the first interna  c06/2000  a164-730 v83 a<p>Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.</p>10aAlgorithms10aCerebral Cortex10aCommunication Aids for Disabled10aDisabled Persons10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aHeetderks, W J1 aMcFarland, Dennis, J.1 aPeckham, P H1 aSchalk, Gerwin1 aDonchin, Emanuel1 aQuatrano, L A1 aRobinson, C J1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/1089617802346nas a2200181   4500008004100000022001400041245008500055210006900140260001200209300001600221490000700237520178000244653002802024100001802052700002602070700002102096856004702117       1998                        eng d  a0003-999300aAnswering questions with an electroencephalogram-based brain-computer interface.0 aAnswering questions with an electroencephalogrambased braincompu  c09/1998  a1029–10330 v793 aOBJECTIVE:
To demonstrate that humans can learn to control selected electroencephalographic components and use that control to answer simple questions.
METHODS:
Four adults (one with amyotrophic lateral sclerosis) learned to use electroencephalogram (EEG) mu rhythm (8 to 12Hz) or beta rhythm (18 to 25Hz) activity over sensorimotor cortex to control vertical cursor movement to targets at the top or bottom edge of a video screen. In subsequent sessions, the targets were replaced with the words YES and NO, and individuals used the cursor to answer spoken YES/NO questions from single- or multiple-topic question sets. They confirmed their answers through the response verification (RV) procedure, in which the word positions were switched and the question was answered again.
RESULTS:
For 5 consecutive sessions after initial question training, individuals were asked an average of 4.0 to 4.6 questions per minute; 64% to 87% of their answers were confirmed by the RV procedure and 93% to 99% of these answers were correct. Performances for single- and multiple-topic question sets did not differ significantly.
CONCLUSIONS:
The results indicate that (1) EEG-based cursor control can be used to answer simple questions with a high degree of accuracy, (2) attention to auditory queries and formulation of answers does not interfere with EEG-based cursor control, (3) question complexity (at least as represented by single versus multiple-topic question sets) does not noticeably affect performance, and (4) the RV procedure improves accuracy as expected. Several options for increasing the speed of communication appear promising. An EEG-based brain-computer interface could provide a new communication and control modality for people with severe motor disabilities.10aUser-Computer Interface1 aMiner, L., A.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/974967801530nas a2200205   4500008004100000022001400041245011100055210006900166260000900235300001400244490000700258520087900265653002801144100002101172700001901193700001801212700002101230700002601251856004701277       1995                        eng d  a0047-655200aEEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.0 aEEGbased brain computer interface BCI Search for optimal electro  c1996  a111–1210 v213 aSeveral laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control.10aUser-Computer Interface1 aPfurtscheller, G1 aFlotzinger, D.1 aPregenzer, M.1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/8776708