02231nas a2200205   4500008004100000245007600041210006900117260001200186520158000198653001401778653000801792653001001800653002201810653003101832100001601863700002501879700002501904700001901929856007701948       2015                        eng d00aCortical alpha activity predicts the confidence in an impending action.0 aCortical alpha activity predicts the confidence in an impending   c07/20153 aWhen we make a decision, we experience a degree of confidence that our choice may lead to a desirable outcome. Recent studies in animals have probed the subjective aspects of the choice confidence using confidence-reporting tasks. These studies showed that estimates of the choice confidence substantially modulate neural activity in multiple regions of the brain. Building on these findings, we investigated the neural representation of the confidence in a choice in humans who explicitly reported the confidence in their choice. Subjects performed a perceptual decision task in which they decided between choosing a button press or a saccade while we recorded EEG activity. Following each choice, subjects indicated whether they were sure or unsure about the choice. We found that alpha activity strongly encodes a subject's confidence level in a forthcoming button press choice. The neural effect of the subjects' confidence was independent of the reaction time and independent of the sensory input modeled as a decision variable. Furthermore, the effect is not due to a general cognitive state, such as reward expectation, because the effect was specifically observed during button press choices and not during saccade choices. The neural effect of the confidence in the ensuing button press choice was strong enough that we could predict, from independent single trial neural signals, whether a subject was going to be sure or unsure of an ensuing button press choice. In sum, alpha activity in human cortex provides a window into the commitment to make a hand movement.10acertainty10aEEG10ahuman10aneural correlates10aperceptual decision-making1 aKubánek, J1 aHill, Jeremy, Jeremy1 aSnyder, Lawrence, H.1 aSchalk, Gerwin  uhttp://journal.frontiersin.org/article/10.3389/fnins.2015.00243/abstract02073nas a2200241   4500008004100000022001400041245011100055210006900166260001200235300001100247490000700258520141600265653001001681653000801691653000901699653000801708653001201716653001101728653000801739100001701747700001901764856004801783       2015                        eng d  a1559-008900aNeuralAct: A Tool to Visualize Electrocortical (ECoG) Activity on a Three-Dimensional Model of the Cortex.0 aNeuralAct A Tool to Visualize Electrocortical ECoG Activity on a  c04/2015  a167-740 v133 a<p>Electrocorticography (ECoG) records neural signals directly from the surface of the cortex. Due to its high temporal and favorable spatial resolution, ECoG has emerged as a valuable new tool in acquiring cortical activity in cognitive and systems neuroscience. Many studies using ECoG visualized topographies of cortical activity or statistical tests on a three-dimensional model of the cortex, but a dedicated tool for this function has not yet been described. In this paper, we describe the NeuralAct package that serves this purpose. This package takes as input the 3D coordinates of the recording sensors, a cortical model in the same coordinate system (e.g., Talairach), and the activation data to be visualized at each sensor. It then aligns the sensor coordinates with the cortical model, convolves the activation data with a spatial kernel, and renders the resulting activations in color on the cortical model. The NeuralAct package can plot cortical activations of an individual subject as well as activations averaged over subjects. It is capable to render single images as well as sequences of images. The software runs under Matlab and is stable and robust. We here provide the tool and describe its visualization capabilities and procedures. The provided package contains thoroughly documented code and includes a simple demo that guides the researcher through the functionality of the tool.</p>10aBrain10aDOT10aECoG10aEEG10aimaging10aMatlab10aMEG1 aKubanek, Jan1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2538164102627nas a2200277   4500008004100000245009500041210006900136260001200205490000600217520181900223653002602042653001402068653000902082653000802091653002802099653000802127100002202135700002002157700001702177700001902194700001902213700002602232700002302258700002002281856004802301       2014                        eng d00aAssessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.0 aAssessing dynamics spatial scale and uncertainty in taskrelated   c03/20140 v83 aThe brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.10acanonical correlation10acoherence10aECoG10aEEG10afunctional connectivity10aMEG1 aStephen, Emily, P1 aLepage, Kyle, Q1 aEden, Uri, T1 aBrunner, Peter1 aSchalk, Gerwin1 aBrumberg, Jonathan, S1 aGuenther, Frank, H1 aKramer, Mark, A  uhttp://www.ncbi.nlm.nih.gov/pubmed/2467829502257nas a2200217   4500008004100000022001400041245016900055210006900224260001200293520155100305653000801856653001301864653000801877653000801885653000801893653002201901653002301923100002001946700002501966856004801991       2013                        eng d  a1559-008900aInteractions Between Pre-Processing and Classification Methods for Event-Related-Potential Classification : Best-Practice Guidelines for Brain-Computer Interfacing.0 aInteractions Between PreProcessing and Classification Methods fo  c04/20133 aDetecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g. visual or tactile), ERP component (e.g. P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a "best-practice" method for ERP detection problems.10aBCI10adecoding10aEEG10aERP10aLDA10aspatial filtering10aspectral filtering1 aFarquhar, Jason1 aHill, Jeremy, Jeremy  uhttp://www.ncbi.nlm.nih.gov/pubmed/2325066801319nas a2200205   4500008004100000022001400041245008200055210006900137260001200206300001400218490000800232520070000240653001500940653002900955653000800984100002600992700002601018700002101044856004801065       2011                        eng d  a1872-678X00aShould the parameters of a BCI translation algorithm be continually adapted?.0 aShould the parameters of a BCI translation algorithm be continua  c07/2011  a103–1070 v1993 aPeople with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures.10aadaptation10abrain-computer interface10aEEG1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2157100401826nas a2200217   4500008004100000022001400041245007300055210006900128260001200197300001600209490000800225520117900233653002901412653000801441653001801449100002501467700002101492700002101513700002601534856004801560       2011                        eng d  a1872-895200aTrained modulation of sensorimotor rhythms can affect reaction time.0 aTrained modulation of sensorimotor rhythms can affect reaction t  c09/2011  a1820–18260 v1223 aOBJECTIVE:
Brain-computer interface (BCI) technology might be useful for rehabilitation of motor function. This speculation is based on the premise that modifying the EEG will modify behavior, a proposition for which there is limited empirical data. The present study examined the possibility that voluntary modulation of sensorimotor rhythm (SMR) can affect motor behavior in normal human subjects.
METHODS:
Six individuals performed a cued-reaction task with variable warning periods. A typical variable foreperiod effect was associated with SMR desynchronization. SMR features that correlated with reaction times were then used to control a two-target cursor movement BCI task. Following successful BCI training, an uncued reaction time task was embedded within the cursor movement task.
RESULTS:
Voluntarily increasing SMR beta rhythms was associated with longer reaction times than decreasing SMR beta rhythms.
CONCLUSIONS:
Voluntary modulation of EEG SMR can affect motor behavior.
SIGNIFICANCE:
These results encourage studies that integrate BCI training into rehabilitation protocols and examine its capacity to augment restoration of useful motor function.10abrain-computer interface10aEEG10aReaction Time1 aBoulay, Chadwick, B.1 aSarnacki, W., A.1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/2141136602079nas a2200205   4500008004100000022001400041245010100055210006900156260001200225300001400237490000700251520145100258653002901709653002801738653000801766653001001774653002001784100002101804856004801825       2010                        eng d  a1940-102700aBrain-computer interface research comes of age: traditional assumptions meet emerging realities.0 aBraincomputer interface research comes of age traditional assump  c11/2010  a351–3530 v423 aBrain-computer interfaces (BCIs) could provide important new communication and control options for people with severe motor disabilities. Most BCI research to date has been based on 4 assumptions that: (a) intended actions are fully represented in the cerebral cortex; (b) neuronal action potentials can provide the best picture of an intended action; (c) the best BCI is one that records action potentials and decodes them; and (d) ongoing mutual adaptation by the BCI user and the BCI system is not very important. In reality, none of these assumptions is presently defensible. Intended actions are the products of many areas, from the cortex to the spinal cord, and the contributions of each area change continually as the CNS adapts to optimize performance. BCIs must track and guide these adaptations if they are to achieve and maintain good performance. Furthermore, it is not yet clear which category of brain signals will prove most effective for BCI applications. In human studies to date, low-resolution electroencephalography-based BCIs perform as well as high-resolution cortical neuron-based BCIs. In sum, BCIs allow their users to develop new skills in which the users control brain signals rather than muscles. Thus, the central task of BCI research is to determine which brain signals users can best control, to maximize that control, and to translate it accurately and reliably into actions that accomplish the users' intentions.10abrain-computer interface10abrain-machine interface10aEEG10ahuman10aneuroprosthesis1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2118435202444nas a2200325   4500008004100000022001400041245011200055210006900167260001200236300001600248490000800264520147000272653002901742653002801771653000801799653002801807653000901835653001901844100001701863700002001880700002501900700002301925700001701948700001901965700002101984700002402005700002102029700002002050856004802070       2010                        eng d  a1872-895200aA novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.0 anovel P300based braincomputer interface stimulus presentation pa  c07/2010  a1109–11200 v1213 aOBJECTIVE:
An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation - the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988).
METHODS:
Using an 8x9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9-12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data.
RESULTS:
Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP.
CONCLUSIONS:
These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability.
SIGNIFICANCE:
The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.10abrain-computer interface10abrain-machine interface10aEEG10aevent-related potential10aP30010aRehabilitation1 aTownsend, G.1 aLaPallo, B., K.1 aBoulay, Chadwick, B.1 aKrusienski, D., J.1 aFrye, G., E.1 aHauser, C., K.1 aSchwartz, N., E.1 aVaughan, Theresa, M1 aWolpaw, Jonathan1 aSellers, E., W.  uhttp://www.ncbi.nlm.nih.gov/pubmed/2034738702473nas a2200289   4500008004100000022001400041245007500055210006900130260001200199300001600211490000800227520163800235653002901873653002801902653000801930653002801938653000901966653001901975100002101994700002402015700001902039700002102058700002102079700001502100700002002115856004802135       2009                        eng d  a1872-895200aToward a high-throughput auditory P300-based brain-computer interface.0 aToward a highthroughput auditory P300based braincomputer interfa  c07/2009  a1252–12610 v1203 aOBJECTIVE:
Brain-computer interface (BCI) technology can provide severely disabled people with non-muscular communication. For those most severely disabled, limitations in eye mobility or visual acuity may necessitate auditory BCI systems. The present study investigates the efficacy of the use of six environmental sounds to operate a 6x6 P300 Speller.
METHODS:
A two-group design was used to ascertain whether participants benefited from visual cues early in training. Group A (N=5) received only auditory stimuli during all 11 sessions, whereas Group AV (N=5) received simultaneous auditory and visual stimuli in initial sessions after which the visual stimuli were systematically removed. Stepwise linear discriminant analysis determined the matrix item that elicited the largest P300 response and thereby identified the desired choice.
RESULTS:
Online results and offline analyses showed that the two groups achieved equivalent accuracy. In the last session, eight of 10 participants achieved 50% or more, and four of these achieved 75% or more, online accuracy (2.8% accuracy expected by chance). Mean bit rates averaged about 2 bits/min, and maximum bit rates reached 5.6 bits/min.
CONCLUSIONS:
This study indicates that an auditory P300 BCI is feasible, that reasonable classification accuracy and rate of communication are achievable, and that the paradigm should be further evaluated with a group of severely disabled participants who have limited visual mobility.
SIGNIFICANCE:
With further development, this auditory P300 BCI could be of substantial value to severely disabled people who cannot use a visual BCI.10abrain-computer interface10abrain-machine interface10aEEG10aevent-related potential10aP30010aRehabilitation1 aKlobassa, D., S.1 aVaughan, Theresa, M1 aBrunner, Peter1 aSchwartz, N., E.1 aWolpaw, Jonathan1 aNeuper, C.1 aSellers, E., W.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1957409102112nas a2200289   4500008004100000022001400041245004800055210004100103260001200144300001200156490000800168520133600176653002201512653002901534653000801563653002001571653001501591653002401606100001901630700001901649700001601668700002301684700002601707700002101733700002001754856004801774       2008                        eng d  a0165-027000aAn auditory brain-computer interface (BCI).0 aauditory braincomputer interface BCI  c01/2008  a43–500 v1673 aBrain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2-3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI.10aauditory feedback10abrain-computer interface10aEEG10alocked-in state10amotivation10asensorimotor rhythm1 aNijboer, Femke1 aFurdea, Adrian1 aGunst, Ingo1 aMellinger, Jürgen1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aKübler, Andrea  uhttp://www.ncbi.nlm.nih.gov/pubmed/1739979702437nas a2200241   4500008004100000022001400041245006700055210006500122260001200187300001400199490000700213520176200220653000801982653000801990653002001998653001902018100002402037700002602061700001802087700001802105700002402123856004802147       2008                        eng d  a0301-051100aElectrophysiological markers of skill-related neuroplasticity.0 aElectrophysiological markers of skillrelated neuroplasticity  c07/2008  a221–2300 v783 aNeuroplasticity involved in acquiring a new cognitive skill was investigated with standard time domain event-related potentials (ERPs) of scalp-recorded electroencephalographic (EEG) activity and frequency domain analysis of EEG oscillations looking at the event-related synchronization (ERS) and desynchronization (ERD) of neural activity. Electroencephalographic activity was recorded before and after practice, while participants performed alphabet addition (i.e., E+3=G, true or false?). Participant's performance became automated with practice through a switch in cognitive strategy from mentally counting-up in the alphabet to retrieving the answer from memory. Time domain analysis of the ERPs revealed a prominent positive peak at approximately 300 ms that was not reactive to problem attributes but was reduced with practice. A second prominent positive peak observed at approximately 500 ms was found to be larger after practice, mainly for problems presented with correct answers. Frequency domain spectral analyses yielded two distinct findings: (1) a frontal midline ERS of theta activity that was greater after practice, and (2) a beta band ERD that increased with problem difficulty before, but not after practice. Because the EEG oscillations were not phase locked to the stimulus, they were viewed as being independent of the time domain results. Consequently, use of time and frequency domain analyses provides a more comprehensive account of the underlying electrophysiological data than either method alone. When used in combination with a well-defined cognitive/behavioral paradigm, this approach serves to constrain the interpretations of EEG data and sets a new standard for studying the neuroplasticity involved in skill acquisition.10aEEG10aERP10aneuroplasticity10askill learning1 aRomero, Stephen, G.1 aMcFarland, Dennis, J.1 aFaust, Robert1 aFarrell, Lori1 aCacace, Anthony, T.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1845586103358nas a2200397   4500008004100000020001800041245009600059210006900155260015500224520204800379653003002427653000802457653001502465653003002480653002302510653002902533653003102562100002002593700001502613700001502628700001302643700001902656700001902675700001402694700001302708700002502721700002302746700001402769700002402783700001502807700002102822700001802843700001802861700002102879856006002900       2007                        eng d  a978026225604900aBrain Computer Interfaces for Communication in Paralysis: a Clinical-Experimental Approach.0 aBrain Computer Interfaces for Communication in Paralysis a Clini  bVirtual Library of Psychology at Saarland University and State Library, GERMANY, PsyDok [http://psydok.sulb.uni-saarland.de/phpoai/oai2.php] (Germany)3 a<p><span style="color: #333333; font-family: sans-serif; font-size: 15px; line-height: 24px;">An overview of different approaches to brain-computer interfaces (BCIs) developed in our laboratory is given. An important clinical application of BCIs is to enable communication or environmental control in severely paralyzed patients. The BCI “Thought-Translation Device (TTD)” allows verbal communication through the voluntary self-regulation of brain signals (e.g., slow cortical potentials (SCPs)), which is achieved by operant feedback training. Humans' ability to self-regulate their SCPs is used to move a cursor toward a target that contains a selectable letter set. Two different approaches were followed to developWeb browsers that could be controlled with binary brain responses. Implementing more powerful classification methods including different signal parameters such as oscillatory features improved our BCI considerably. It was also tested on signals with implanted electrodes. Most BCIs provide the user with a visual feedback interface. Visually impaired patients require an auditory feedback mode. A procedure using auditory (sonified) feedback of multiple EEG parameters was evaluated. Properties of the auditory systems are reported and the results of two experiments with auditory feedback are presented. Clinical data of eight ALS patients demonstrated that all patients were able to acquire efficient brain control of one of the three available BCI systems (SCP, µ-rhythm, and P300), most of them used the SCP-BCI. A controlled comparison of the three systems in a group of ALS patients, however, showed that P300-BCI and the µ-BCI are faster and more easily acquired than SCP-BCI, at least in patients with some rudimentary motor control left. Six patients who started BCI training after entering the completely locked-in state did not achieve reliable communication skills with any BCI system. One completely locked-in patient was able t o communicate shortly with a ph-meter, but lost control afterward.</span></p>10abrain-computer interfaces10aEEG10aexperiment10aMedical sciences Medicine10aparalyzed patients10aslow cortical potentials10aThought-Translation Device1 aHinterberger, T1 aNijboer, F1 aKübler, A1 aMatuz, T1 aFurdea, Adrian1 aMochty, Ursula1 aJordan, M1 aLal, T N1 aHill, Jeremy, Jeremy1 aMellinger, Jürgen1 aBensch, M1 aTangermann, Michael1 aWidmann, G1 aElger, Christian1 aRosenstiel, W1 aSchölkopf, B1 aBirbaumer, Niels  uhttp://psydok.sulb.uni-saarland.de/volltexte/2008/2154/01471nas a2200325   4500008004100000022001400041245006200055210005900117260001200176300001400188490000600202520059000208653000800798653002800806653000800834653000800842653002600850653002900876653002800905653000800933653000800941653002300949653002800972653001001000653001101010100002501021700003001046700002101076856004801097       2007                        eng d  a1743-444000aBrain-computer interface systems: progress and prospects.0 aBraincomputer interface systems progress and prospects  c07/2007  a463–4740 v43 aBrain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.10aALS10aassistive communication10aBCI10aBMI10abrain-acuated control10abrain-computer interface10abrain-machine interface10aEEG10aERP10alocked-in syndrome10aslow cortical potential10aSSVEP10aStroke1 aAllison, Brendan, Z.1 aWolpaw, Elizabeth, Winter1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1760568203537nas a2200253   4500008004100000022001400041245009500055210006900150260001200219300001200231490000800243520277200251653002903023653000803052653002703060653001303087653001403100653002403114100002603138700002603164700002403190700002103214856004803235       2005                        eng d  a1388-245700aBrain-computer interface (BCI) operation: signal and noise during early training sessions.0 aBraincomputer interface BCI operation signal and noise during ea  c01/2005  a56–620 v1163 aOBJECTIVE:
People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the electroencephalogram (EEG) recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The recorded signal may also contain electromyogram (EMG) and other non-EEG artifacts. This study examines the presence and characteristics of EMG contamination during new users' initial brain-computer interface (BCI) training sessions, as they first attempt to acquire control over mu or beta rhythm amplitude and to use that control to move a cursor to a target.
METHODS:
In the standard one-dimensional format, a target appears along the right edge of the screen and 1s later the cursor appears in the middle of the left edge and moves across the screen at a fixed rate with its vertical movement controlled by a linear function of mu or beta rhythm amplitude. In the basic two-choice version, the target occupies the upper or lower half of the right edge. The user's task is to move the cursor vertically so that it hits the target when it reaches the right edge. The present data comprise the first 10 sessions of BCI training from each of 7 users. Their data were selected to illustrate the variations seen in EMG contamination across users.
RESULTS:
Five of the 7 users learned to change rhythm amplitude appropriately, so that the cursor hit the target. Three of these 5 showed no evidence of EMG contamination. In the other two of these 5, EMG was prominent in early sessions, and tended to be associated with errors rather than with hits. As EEG control improved over the 10 sessions, this EMG contamination disappeared. In the remaining two users, who never acquired actual EEG control, EMG was prominent in initial sessions and tended to move the cursor to the target. This EMG contamination was still detectable by Session 10.
CONCLUSIONS:
EMG contamination arising from cranial muscles is often present early in BCI training and gradually wanes. In those users who eventually acquire EEG control, early target-related EMG contamination may be most prominent for unsuccessful trials, and may reflect user frustration. In those users who never acquire EEG control, EMG may initially serve to move the cursor toward the target. Careful and comprehensive topographical and spectral analyses throughout user training are essential for detecting EMG contamination and differentiating between cursor control provided by EEG control and cursor control provided by EMG contamination.
SIGNIFICANCE:
Artifacts such as EMG are common in EEG recordings. Comprehensive spectral and topographical analyses are necessary to detect them and ensure that they do not masquerade as, or interfere with acquisition of, actual EEG-based cursor control.10abrain-computer interface10aEEG10aElectroencephalography10aLearning10amu rhythm10asensorimotor cortex1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1558918401351nas a2200409   4500008004100000020001800041245007500059210006900134260003300203653002700236653003000263653001900293653002900312653002500341653003300366653003400399653000800433653002700441653003900468653002100507653001700528653002000545653002900565653003000594653001500624653001800639653002300657653002600680653001100706653002300717653004000740100002400780700001900804700002600823700002100849856007100870       2005                        eng d  a0-7803-8710-400aTracking of the mu rhythm using an empirically derived matched filter.0 aTracking of the mu rhythm using an empirically derived matched f  aArlington, VAbIEEEc03/200510abioelectric potentials10aBrain Computer Interfaces10abrain modeling10abrain-computer interface10acommunication device10acommunication system control10acortical mu rhythm modulation10aEEG10aElectroencephalography10aempirically derived matched filter10ahandicapped aids10alaboratories10amatched filters10amedical signal detection10amedical signal processing10amonitoring10amotor imagery10amu rhythm tracking10anoninvasive treatment10arhythm10asynchronous motors10atwo-dimensional cursor control data1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=141955902685nas a2200433   4500008004100000022001400041245011000055210006900165260001200234300001600246490000700262520140200269653003101671653000801702653001601710653002901726653000801755653000801763653002801771653003601799653001401835653000901849653001901858653003201877653002901909100002401938700002601962700001901988700002402007700001902031700002102050700002002071700002002091700002402111700002402135700002302159700002102182856004802203       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  a1044–10510 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.10aaugmentative communication10aBCI10abeta-rhythm10abrain-computer interface10aEEG10aERP10aimagined hand movements10alateralized readiness potential10amu-rhythm10aP30010aRehabilitation10asingle-trial classification10aslow cortical potentials1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aCurio, Gabriel1 aVaughan, Theresa, M1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aNeuper, Christa1 aPfurtscheller, Gert1 aHinterberger, Thilo1 aSchröder, Michael1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/1518887602265nas a2200241   4500008004100000022001400041245007900055210006900134260001200203300001400215490000700229520157600236653001601812653000801828653001201836653001401848653002401862100002601886700001801912700002401930700002101954856004801975       2000                        eng d  a0896-026700aMu and beta rhythm topographies during motor imagery and actual movements.0 aMu and beta rhythm topographies during motor imagery and actual   c03/2000  a177–1860 v123 aPeople can learn to control the 8-12 Hz mu rhythm and/or the 18-25 Hz beta rhythm in the EEG recorded over sensorimotor cortex and use it to control a cursor on a video screen. Subjects often report using motor imagery to control cursor movement, particularly early in training. We compared in untrained subjects the EEG topographies associated with actual hand movement to those associated with imagined hand movement. Sixty-four EEG channels were recorded while each of 33 adults moved left- or right-hand or imagined doing so. Frequency-specific differences between movement or imagery and rest, and between right- and left-hand movement or imagery, were evaluated by scalp topographies of voltage and r spectra, and principal component analysis. Both movement and imagery were associated with mu and beta rhythm desynchronization. The mu topographies showed bilateral foci of desynchronization over sensorimotor cortices, while the beta topographies showed peak desynchronization over the vertex. Both mu and beta rhythm left/right differences showed bilateral central foci that were stronger on the right side. The independence of mu and beta rhythms was demonstrated by differences for movement and imagery for the subjects as a group and by principal components analysis. The results indicated that the effects of imagery were not simply an attenuated version of the effects of movement. They supply evidence that motor imagery could play an important role in EEG-based communication, and suggest that mu and beta rhythms might provide independent control signals.10abeta rhythm10aEEG10aimagery10amu rhythm10asensorimotor cortex1 aMcFarland, Dennis, J.1 aMiner, L., A.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1079168102018nas a2200265   4500008004100000022001400041245006200055210005600117260001200173300001400185490000700199520128900206653001801495653002101513653000801534653001401542653002501556653001501581653002401596100002101620700002601641700001701667700002101684856004701705       1991                        eng d  a0013-469400aAn EEG-based brain-computer interface for cursor control.0 aEEGbased braincomputer interface for cursor control  c03/1991  a252–2590 v783 aThis study began development of a new communication and control modality for individuals with severe motor deficits. We trained normal subjects to use the 8-12 Hz mu rhythm recorded from the scalp over the central sulcus of one hemisphere to move a cursor from the center of a video screen to a target located at the top or bottom edge. Mu rhythm amplitude was assessed by on-line frequency analysis and translated into cursor movement: larger amplitudes moved the cursor up and smaller amplitudes moved it down. Over several weeks, subjects learned to change mu rhythm amplitude quickly and accurately, so that the cursor typically reached the target in 3 sec. The parameters that translated mu rhythm amplitudes into cursor movements were derived from evaluation of the distributions of amplitudes in response to top and bottom targets. The use of these distributions was a distinctive feature of this study and the key factor in its success. Refinements in training procedures and in the distribution-based method used to translate mu rhythm amplitudes into cursor movements should further improve this 1-dimensional control. Achievement of 2-dimensional control is under study. The mu rhythm may provide a significant new communication and control option for disabled individuals.10aCommunication10acomputer control10aEEG10amu rhythm10aoperant conditioning10aprosthesis10asensorimotor rhythm1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aNeat, G., W.1 aForneris, C., A.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1707798