03989nas a2200193 4500008004100000245005800041210005600099260001200155490000800167520340900175653000803584653000803592653002903600653003003629100001703659700002703676700001703703856007503720 2020 eng d00aBrain-computer interfaces: Definitions and principles0 aBraincomputer interfaces Definitions and principles c03/20200 v1683 aThroughout life, the central nervous system (CNS) interacts with the world and with the body by activating muscles and excreting hormones. In contrast, brain-computer interfaces (BCIs) quantify CNS activity and translate it into new artificial outputs that replace, restore, enhance, supplement, or improve the natural CNS outputs. BCIs thereby modify the interactions between the CNS and the environment. Unlike the natural CNS outputs that come from spinal and brainstem motoneurons, BCI outputs come from brain signals that represent activity in other CNS areas, such as the sensorimotor cortex. If BCIs are to be useful for important communication and control tasks in real life, the CNS must control these brain signals nearly as reliably and accurately as it controls spinal motoneurons. To do this, they might, for example, need to incorporate software that mimics the function of the subcortical and spinal mechanisms that participate in normal movement control. The realization of high reliability and accuracy is perhaps the most difficult and critical challenge now facing BCI research and development. The ongoing adaptive modifications that maintain effective natural CNS outputs take place primarily in the CNS. The adaptive modifications that maintain effective BCI outputs can also take place in the BCI. This means that the BCI operation depends on the effective collaboration of two adaptive controllers, the CNS and the BCI. Realization of this second adaptive controller, the BCI, and management of its interactions with concurrent adaptations in the CNS comprise another complex and critical challenge for BCI development. BCIs can use different kinds of brain signals recorded in different ways from different brain areas. Decisions about which signals recorded in which ways from which brain areas should be selected for which applications are empirical questions that can only be properly answered by experiments. BCIs, like other communication and control technologies, often face artifacts that contaminate or imitate their chosen signals. Noninvasive BCIs (e.g., EEG- or fNIRS-based) need to take special care to avoid interpreting nonbrain signals (e.g., cranial EMG) as brain signals. This typically requires comprehensive topographical and spectral evaluations. In theory, the outputs of BCIs can select a goal or control a process. In the future, the most effective BCIs will probably be those that combine goal selection and process control so as to distribute control between the BCI and the application in a fashion suited to the current action. Through such distribution, BCIs may most effectively imitate natural CNS operation. The primary measure of BCI development is the extent to which BCI systems benefit people with neuromuscular disorders. Thus, BCI clinical evaluation, validation, and dissemination is a key step. It is at the same time a complex and difficult process that depends on multidisciplinary collaboration and management of the demanding requirements of clinical studies. Twenty-five years ago, BCI research was an esoteric endeavor pursued in only a few isolated laboratories. It is now a steadily growing field that engages many hundreds of scientists, engineers, and clinicians throughout the world in an increasingly interconnected community that is addressing the key issues and pursuing the high potential of BCI technology.10aBCI10aBMI10abrain-computer interface10abrain–machine interface1 aWolpaw, J.R.1 aMillán, José, del R.1 aRamsey, N.F. uhttps://www.sciencedirect.com/science/article/pii/B978044463934900002001542nas a2200193 4500008004100000022001400041245004000055210003800095260000800133300001200141490000600153520102900159653002901188653002001217653001901237100002201256700002101278856004901299 2017 eng d a2468-451100aEEG-based brain-computer interfaces0 aEEGbased braincomputer interfaces cOct a194-2000 v43 aBrain–Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more comfortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.10abrain-computer interface10aneurotechnology10aRehabilitation1 aMcFarland, D., J.1 aWolpaw, Jonathan uhttps://www.ncbi.nlm.nih.gov/pubmed/2143819302272nas a2200277 4500008004100000245008600041210006900127260001200196520140600208653003301614653002901647653002001676653000901696653002501705653002401730653002001754653002201774100001401796700001401810700002401824700001501848700001901863700001901882700001601901856007701917 2015 eng d00aBrain-to-text: Decoding spoken sentences from phone representations in the brain.0 aBraintotext Decoding spoken sentences from phone representations c06/20153 aIt has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To- Text system described in this paper represents an important step toward human-machine communication based on imagined speech.10aautomatic speech recognition10abrain-computer interface10abroadband gamma10aECoG10aElectrocorticography10apattern recognition10aspeech decoding10aspeech production1 aHerff, C.1 aHeger, D.1 ade Pesters, Adriana1 aTelaar, D.1 aBrunner, Peter1 aSchalk, Gerwin1 aSchultz, T. uhttp://journal.frontiersin.org/article/10.3389/fnins.2015.00217/abstract02314nas a2200229 4500008004100000022001400041245010300055210006900158260001200227300001200239490000800251520162300259653002901882653001101911653001301922653001801935653001601953100002501969700002101994700002102015856004802036 2015 eng d a1522-159800aElectrocorticographic activity over sensorimotor cortex and motor function in awake behaving rats.0 aElectrocorticographic activity over sensorimotor cortex and moto c04/2015 a2232-410 v1133 a

Sensorimotor cortex exerts both short-term and long-term control over the spinal reflex pathways that serve motor behaviors. Better understanding of this control could offer new possibilities for restoring function after central nervous system trauma or disease. We examined the impact of ongoing sensorimotor cortex (SMC) activity on the largely monosynaptic pathway of the H-reflex, the electrical analog of the spinal stretch reflex. In 41 awake adult rats, we measured soleus electromyographic (EMG) activity, the soleus H-reflex, and electrocorticographic activity over the contralateral SMC while rats were producing steady-state soleus EMG activity. Principal component analysis of electrocorticographic frequency spectra before H-reflex elicitation consistently revealed three frequency bands: μβ (5-30 Hz), low γ (γ1; 40-85 Hz), and high γ (γ2; 100-200 Hz). Ongoing (i.e., background) soleus EMG amplitude correlated negatively with μβ power and positively with γ1 power. In contrast, H-reflex size correlated positively with μβ power and negatively with γ1 power, but only when background soleus EMG amplitude was included in the linear model. These results support the hypothesis that increased SMC activation (indicated by decrease in μβ power and/or increase in γ1 power) simultaneously potentiates the H-reflex by exciting spinal motoneurons and suppresses it by decreasing the efficacy of the afferent input. They may help guide the development of new rehabilitation methods and of brain-computer interfaces that use SMC activity as a substitute for lost or impaired motor outputs.

10abrain-computer interface10acortex10aH-Reflex10aMotor control10aSpinal Cord1 aBoulay, Chadwick, B.1 aChen, Xiang Yang1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2563207601705nas a2200157 4500008004100000022001400041245008200055210006900137260001200206520117900218653002901397653002501426653002201451100002601473856004801499 2014 eng d a1872-769700aThe advantages of the surface Laplacian in brain-computer interface research.0 aadvantages of the surface Laplacian in braincomputer interface r c08/20143 a

Brain-computer interface (BCI) systems frequently use signal processing methods, such as spatial filtering, to enhance performance. The surface Laplacian can reduce spatial noise and aid in identification of sources. In BCI research, these two functions of the surface Laplacian correspond to prediction accuracy and signal orthogonality. In the present study, an off-line analysis of data from a sensorimotor rhythm-based BCI task dissociated these functions of the surface Laplacian by comparing nearest-neighbor and next-nearest neighbor Laplacian algorithms. The nearest-neighbor Laplacian produced signals that were more orthogonal while the next-nearest Laplacian produced signals that resulted in better accuracy. Both prediction and signal identification are important for BCI research. Better prediction of user's intent produces increased speed and accuracy of communication and control. Signal identification is important for ruling out the possibility of control by artifacts. Identifying the nature of the control signal is relevant both to understanding exactly what is being studied and in terms of usability for individuals with limited motor control.

10abrain-computer interface10asensorimotor rhythms10asurface laplacian1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2509128602766nas a2200205 4500008004100000245009500041210007100136260001200207490000700219520209000226653002902316653002102345653003002366653002102396653002702417100002502444700002402469700001902493856004802512 2014 eng d00aA general method for assessing brain–computer interface performance and its limitations.0 ageneral method for assessing brain–computer interface performanc c03/20140 v113 aObjective. When researchers evaluate brain–computer interface (BCI) systems, we want quantitative answers to questions such as: How good is the system's performance? How good does it need to be? and: Is it capable of reaching the desired level in future? In response to the current lack of objective, quantitative, study-independent approaches, we introduce methods that help to address such questions. We identified three challenges: (I) the need for efficient measurement techniques that adapt rapidly and reliably to capture a wide range of performance levels; (II) the need to express results in a way that allows comparison between similar but non-identical tasks; (III) the need to measure the extent to which certain components of a BCI system (e.g. the signal processing pipeline) not only support BCI performance, but also potentially restrict the maximum level it can reach. Approach. For challenge (I), we developed an automatic staircase method that adjusted task difficulty adaptively along a single abstract axis. For challenge (II), we used the rate of information gain between two Bernoulli distributions: one reflecting the observed success rate, the other reflecting chance performance estimated by a matched random-walk method. This measure includes Wolpaw's information transfer rate as a special case, but addresses the latter's limitations including its restriction to item-selection tasks. To validate our approach and address challenge (III), we compared four healthy subjects' performance using an EEG-based BCI, a 'Direct Controller' (a high-performance hardware input device), and a 'Pseudo-BCI Controller' (the same input device, but with control signals processed by the BCI signal processing pipeline). Main results. Our results confirm the repeatability and validity of our measures, and indicate that our BCI signal processing pipeline reduced attainable performance by about 33% (21 bits/min). Significance. Our approach provides a flexible basis for evaluating BCI performance and its limitations, across a wide range of tasks and task difficulties.10abrain-computer interface10ainformation gain10ainformation transfer rate10aNeuroprosthetics10aperformance evaluation1 aHill, Jeremy, Jeremy1 aHäuser, Ann-Katrin1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2465840601857nas a2200421 4500008004100000022001400041245008900055210006900144260001200213300001100225490000700236520059500243653001800838653002900856653003500885653002500920653002300945653004300968653003201011653002101043653002201064653001801086100001801104700001901122700002001141700001701161700002401178700001901202700002101221700002001242700002301262700002201285700001601307700002401323700002101347700001901368856004801387 2014 eng d a1525-506900aProceedings of the Fifth International Workshop on Advances in Electrocorticography.0 aProceedings of the Fifth International Workshop on Advances in E c12/2014 a183-920 v413 a

The Fifth International Workshop on Advances in Electrocorticography convened in San Diego, CA, on November 7-8, 2013. Advancements in methodology, implementation, and commercialization across both research and in the interval year since the last workshop were the focus of the gathering. Electrocorticography (ECoG) is now firmly established as a preferred signal source for advanced research in functional, cognitive, and neuroprosthetic domains. Published output in ECoG fields has increased tenfold in the past decade. These proceedings attempt to summarize the state of the art.

10aBrain Mapping10abrain-computer interface10aelectrical stimulation mapping10aElectrocorticography10afunctional mapping10aGamma-frequency electroencephalography10aHigh-frequency oscillations10aNeuroprosthetics10aSeizure detection10aSubdural grid1 aRitaccio, A L1 aBrunner, Peter1 aGunduz, Aysegul1 aHermes, Dora1 aHirsch, Lawrence, J1 aJacobs, Joshua1 aKamada, Kyousuke1 aKastner, Sabine1 aKnight, Robert, T.1 aLesser, Ronald, P1 aMiller, Kai1 aSejnowski, Terrence1 aWorrell, Gregory1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2546121301885nas a2200193 4500008004100000022001400041245009100055210006900146260001200215300001600227490000800243520126500251653002901516653002201545653002601567653002401593100002601617856004801643 2013 eng d a1872-895200aCharacterizing multivariate decoding models based on correlated EEG spectral features.0 aCharacterizing multivariate decoding models based on correlated  c07/2013 a1297–13020 v1243 aOBJECTIVE: Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. METHODS: Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). RESULTS: The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. CONCLUSIONS: Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. SIGNIFICANCE: While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated.10abrain-computer interface10amulticollinearity10amultivariate decoding10asensorimotor rhythm1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2346626702832nas a2200253 4500008004100000022001400041245012700055210006900182260001200251490000600263520200400269653002302273653003802296653002902334653002302363653001702386653001702403100002502420700002102445700002402466700002102490700001902511856004802530 2012 eng d a1662-453X00aCommunication and control by listening: towards optimal design of a two-class auditory streaming brain-computer interface.0 aCommunication and control by listening towards optimal design of c12/20120 v63 aMost brain-computer interface (BCI) systems require users to modulate brain signals in response to visual stimuli. Thus, they may not be useful to people with limited vision, such as those with severe paralysis. One important approach for overcoming this issue is auditory streaming, an approach whereby a BCI system is driven by shifts of attention between two simultaneously presented auditory stimulus streams. Motivated by the long-term goal of translating such a system into a reliable, simple yes-no interface for clinical usage, we aim to answer two main questions. First, we asked which of two previously published variants provides superior performance: a fixed-phase (FP) design in which the streams have equal period and opposite phase, or a drifting-phase (DP) design where the periods are unequal. We found FP to be superior to DP (p = 0.002): average performance levels were 80 and 72% correct, respectively. We were also able to show, in a pilot with one subject, that auditory streaming can support continuous control and neurofeedback applications: by shifting attention between ongoing left and right auditory streams, the subject was able to control the position of a paddle in a computer game. Second, we examined whether the system is dependent on eye movements, since it is known that eye movements and auditory attention may influence each other, and any dependence on the ability to move one’s eyes would be a barrier to translation to paralyzed users. We discovered that, despite instructions, some subjects did make eye movements that were indicative of the direction of attention. However, there was no correlation, across subjects, between the reliability of the eye movement signal and the reliability of the BCI system, indicating that our system was configured to work independently of eye movement. Together, these findings are an encouraging step forward toward BCIs that provide practical communication and control options for the most severely paralyzed users. 10aauditory attention10aauditory event-related potentials10abrain-computer interface10adichotic listening10aN1 potential10aP3 potential1 aHill, Jeremy, Jeremy1 aMoinuddin, Aisha1 aHäuser, Ann-Katrin1 aKienzle, Stephan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2326731201889nas a2200469 4500008004100000022001400041245008900055210006900144260001200213300001100225490000700236520052900243653001800772653002900790653002500819653004300844653003100887653002100918653002200939653001800961100001800979700002300997700002001020700001901040700001801059700002201077700002001099700001701119700002301136700002001159700001601179700001801195700002101213700001901234700001701253700001901270700002201289700002001311700002101331700001901352856004801371 2012 eng d a1525-506900aProceedings of the Third International Workshop on Advances in Electrocorticography.0 aProceedings of the Third International Workshop on Advances in E c12/2012 a605-130 v253 aThe Third International Workshop on Advances in Electrocorticography (ECoG) was convened in Washington, DC, on November 10-11, 2011. As in prior meetings, a true multidisciplinary fusion of clinicians, scientists, and engineers from many disciplines gathered to summarize contemporary experiences in brain surface recordings. The proceedings of this meeting serve as evidence of a very robust and transformative field but will yet again require revision to incorporate the advances that the following year will surely bring.10aBrain Mapping10abrain-computer interface10aElectrocorticography10aGamma-frequency electroencephalography10ahigh-frequency oscillation10aNeuroprosthetics10aSeizure detection10aSubdural grid1 aRitaccio, A L1 aBeauchamp, Michael1 aBosman, Conrado1 aBrunner, Peter1 aChang, Edward1 aCrone, Nathan, E.1 aGunduz, Aysegul1 aGupta, Disha1 aKnight, Robert, T.1 aLeuthardt, Eric1 aLitt, Brian1 aMoran, Daniel1 aOjemann, Jeffrey1 aParvizi, Josef1 aRamsey, Nick1 aRieger, Jochem1 aViventi, Jonathan1 aVoytek, Bradley1 aWilliams, Justin1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2316009601769nas a2200373 4500008004100000245003800041210003700079260001200116300000900128490000600137520083700143653000800980653002900988653001601017100001801033700001601051700001501067700002101082700002101103700002101124700001201145700001501157700001601172700002001188700001301208700002101221700001501242700001901257700001601276700001601292700001501308700002401323856004801347 2012 eng d00aReview of the BCI Competition IV.0 aReview of the BCI Competition IV c07/2012 a1-310 v63 aThe BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.10aBCI10abrain-computer interface10acompetition1 aTangermann, M1 aMuller, K R1 aAertsen, A1 aBirbaumer, Niels1 aBraun, Christoph1 aBrunner, Clemens1 aLeeb, R1 aMehring, C1 aMiller, K J1 aMueller-Putz, G1 aNolte, G1 aPfurtscheller, G1 aPreissl, H1 aSchalk, Gerwin1 aSchlögl, A1 aVidaurre, C1 aWaldert, S1 aBlankertz, Benjamin uhttp://www.ncbi.nlm.nih.gov/pubmed/2281165702447nas a2200229 4500008004100000022001400041245007700055210006700132260001200199300001400211490000800225520176000233653002901993653002002022653000902042100002602051700002602077700002102103700002402124700002102148856004802169 2011 eng d a1872-895200aThe P300-based brain-computer interface (BCI): effects of stimulus rate.0 aP300based braincomputer interface BCI effects of stimulus rate c04/2011 a731–7370 v1223 aOBJECTIVE: Brain-computer interface technology can restore communication and control to people who are severely paralyzed. We have developed a non-invasive BCI based on the P300 event-related potential that uses an 8×9 matrix of 72 items that flash in groups of 6. Stimulus presentation rate (i.e., flash rate) is one of several parameters that could affect the speed and accuracy of performance. We studied performance (i.e., accuracy and characters/min) on copy spelling as a function of flash rate. METHODS: In the first study of six BCI users, stimulus-on and stimulus-off times were equal and flash rate was 4, 8, 16, or 32 Hz. In the second study of five BCI users, flash rate was varied by changing either the stimulus-on or stimulus-off time. RESULTS: For all users, lower flash rates gave higher accuracy. The flash rate that gave the highest characters/min varied across users, ranging from 8 to 32 Hz. However, variations in stimulus-on and stimulus-off times did not themselves significantly affect accuracy. Providing feedback did not affect results in either study suggesting that offline analyses should readily generalize to online performance. However there do appear to be session-specific effects that can influence the generalizability of classifier results. CONCLUSIONS: The results show that stimulus presentation (i.e., flash) rate affects the accuracy and speed of P300 BCI performance. SIGNIFICANCE: These results extend the range over which slower flash rates increase the amplitude of the P300. Considering also presentation time, the optimal rate differs among users, and thus should be set empirically for each user. Optimal flash rate might also vary with other parameters such as the number of items in the matrix.10abrain-computer interface10aneuroprosthesis10aP3001 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aTownsend, George1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2106797002550nas a2200253 4500008004100000022001400041245009200055210006900147260001200216300000800228490000600236520180100242653002902043653002302072653002602095653001902121653002102140653002002161100001802181700001002199700002002209700001902229856004802248 2011 eng d a1662-453X00aPrior knowledge improves decoding of finger flexion from electrocorticographic signals.0 aPrior knowledge improves decoding of finger flexion from electro c11/2011 a1270 v53 a

Brain-computer interfaces (BCIs) use brain signals to convey a user's intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these studies usually employed classical classification/regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target movement parameter. In this paper, we incorporate prior knowledge using a Bayesian decoding method, and use it to decode finger flexion from ECoG signals. Specifically, we exploit the constraints that govern finger flexion and incorporate these constraints in the construction, structure, and the probabilistic functions of the prior model of a switched non-parametric dynamic system (SNDS). Given a measurement model resulting from a traditional linear regression method, we decoded finger flexion using posterior estimation that combined the prior and measurement models. Our results show that the application of the Bayesian decoding model, which incorporates prior knowledge, improves decoding performance compared to the application of a linear regression model, which does not incorporate prior knowledge. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation.

10abrain-computer interface10adecoding algorithm10aelectrocorticographic10afinger flexion10amachine learning10aprior knowledge1 aWang, Zuoguan1 aJi, Q1 aMiller, John, W1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2214494403409nas a2200253 4500008004100000022001400041245009700055210006900152260001200221300000600233490000600239520266600245653002902911653002502940653002802965653000902993653001203002100001903014700001803033700002203051700001503073700001903088856004803107 2011 eng d a1662-453X00aRapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG).0 aRapid Communication with a P300 Matrix Speller Using Electrocort c02/2011 a50 v53 a

brain-computer interface (BCI) can provide a non-muscular communication channel to severely disabled people. One particular realization of a BCI is the P300 matrix speller that was originally described by Farwell and Donchin (1988). This speller uses event-related potentials (ERPs) that include the P300 ERP. All previous online studies of the P300 matrix speller used scalp-recorded electroencephalography (EEG) and were limited in their communication performance to only a few characters per minute. In our study, we investigated the feasibility of using electrocorticographic (ECoG) signals for online operation of the matrix speller, and determined associated spelling rates. We used the matrix speller that is implemented in the BCI2000 system. This speller used ECoG signals that were recorded from frontal, parietal, and occipital areas in one subject. This subject spelled a total of 444 characters in online experiments. The results showed that the subject sustained a rate of 17 characters/min (i.e., 69 bits/min), and achieved a peak rate of 22 characters/min (i.e., 113 bits/min). Detailed analysis of the results suggests that ERPs over visual areas (i.e., visual evoked potentials) contribute significantly to the performance of the matrix speller BCI system. Our results also point to potential reasons for the apparent advantages in spelling performance of ECoG compared to EEG. Thus, with additional verification in more subjects, these results may further extend the communication options for people with serious neuromuscular disabilities.

10abrain-computer interface10aElectrocorticography10aevent-related potential10aP30010aspeller1 aBrunner, Peter1 aRitaccio, A L1 aEmrich, Joseph, F1 aBischof, H1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2136935101319nas 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/2034738701207nas a2200181 4500008004100000020002200041245003200063210003000095260001900125520069300144653000800837653002900845653002300874653002200897100001900919700001900938856006800957 2009 eng d a978-3-642-02811-300aBrain-Computer Interaction.0 aBrainComputer Interaction bSpringerc20093 a

Detection and automated interpretation of attention-related or intention-related brain activity carries significant promise for many military and civilian applications. This interpretation of brain activity could provide information about a person’s intended movements, imagined movements, or attentional focus, and thus could be valuable for optimizing or replacing traditional motor-based communication between a person and a computer or other output devices. We describe here the objective and preliminary results of our studies in this area.

10aBCI10abrain-computer interface10aneural engineering10aneural prosthesis1 aBrunner, Peter1 aSchalk, Gerwin uhttp://link.springer.com/chapter/10.1007%2F978-3-642-02812-0_8103691nas a2200193 4500008004100000245013500041210006900176260001200245490000700257520302900264653002903293653002503322653001703347100001903364700002903383700002003412700001803432856004703450 2009 eng d00aMicroscale recording from human motor cortex: implications for minimally invasive electrocorticographic brain-computer interfaces.0 aMicroscale recording from human motor cortex implications for mi c07/20090 v273 a

OBJECT: 

There is a growing interest in the use of recording from the surface of the brain, known as electrocorticography (ECoG), as a practical signal platform for brain-computer interface application. The signal has a combination of high signal quality and long-term stability that may be the ideal intermediate modality for future application. The research paradigm for studying ECoG signals uses patients requiring invasive monitoring for seizure localization. The implanted arrays span cortex areas on the order of centimeters. Currently, it is unknown what level of motor information can be discerned from small regions of human cortex with microscale ECoG recording.

METHODS: 

In this study, a patient requiring invasive monitoring for seizure localization underwent concurrent implantation with a 16-microwire array (1-mm electrode spacing) placed over primary motor cortex. Microscale activity was recorded while the patient performed simple contra- and ipsilateral wrist movements that were monitored in parallel with electromyography. Using various statistical methods, linear and nonlinear relationships between these microcortical changes and recorded electromyography activity were defined.

RESULTS: 

Small regions of primary motor cortex (< 5 mm) carry sufficient information to separate multiple aspects of motor movements (that is, wrist flexion/extension and ipsilateral/contralateral movements).

CONCLUSIONS: 

These findings support the conclusion that small regions of cortex investigated by ECoG recording may provide sufficient information about motor intentions to support brain-computer interface operations in the future. Given the small scale of the cortical region required, the requisite implanted array would be minimally invasive in terms of surgical placement of the electrode array.

10abrain-computer interface10aElectrocorticography10aMotor Cortex1 aLeuthardt, E C1 aFreudenberg, Zachary, V.1 aBundy, David, T1 aRoland, Jarod uhttp://dx.doi.org/10.3171/2009.4.FOCUS098001741nas a2200253 4500008004100000022001400041245008200055210006900137260001200206300001400218490000700232520097500239653000801214653002901222653002201251653002401273100003201297700002601329700002001355700002401375700001901399700002101418856004801439 2009 eng d a1873-624600aA scanning protocol for a sensorimotor rhythm-based brain-computer interface.0 ascanning protocol for a sensorimotor rhythmbased braincomputer i c02/2009 a169–1750 v803 aThe scanning protocol is a novel brain-computer interface (BCI) implementation that can be controlled with sensorimotor rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of 10 naive users trained for ten 30 min sessions over 5 weeks. User performance improved significantly (p<0.001) over the sessions and ranged from 30 to 80% mean accuracy of the last three sessions (chance accuracy=25%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections.10aBCI10abrain-computer interface10ascanning protocol10asensorimotor rhythm1 aFriedrich, Elisabeth, V. C.1 aMcFarland, Dennis, J.1 aNeuper, Christa1 aVaughan, Theresa, M1 aBrunner, Peter1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1878660302473nas 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/1739979702392nas a2200361 4500008004100000022001400041245008900055210006900144260001200213300001600225490000800241520135100249653003401600653002901634653002501663653002901688653000901717653001901726100001601745700002001761700001701781700001901798700001301817700001901830700001401849700001501863700002301878700002401901700002101925700002101946700001501967856004801982 2008 eng d a1388-245700aA P300-based brain-computer interface for people with amyotrophic lateral sclerosis.0 aP300based braincomputer interface for people with amyotrophic la c08/2008 a1909–19160 v1193 aOBJECTIVE: The current study evaluates the efficacy of a P300-based brain-computer interface (BCI) communication device for individuals with advanced ALS. METHODS: Participants attended to one cell of a N x N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback. RESULTS: In Phase I, six participants used a 6 x 6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62% and 82%, respectively. In Phase II, four participants used either a 6 x 6 or a 7 x 7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79%. The amplitude and latency of the P300 remained stable over 40 weeks. CONCLUSIONS: Participants could communicate with the P300-based BCI and performance was stable over many months. SIGNIFICANCE: BCIs could provide an alternative communication and control technology in the daily lives of people severely disabled by ALS.10aAmyotrophic Lateral Sclerosis10abrain-computer interface10aelectroencephalogram10aevent-related potentials10aP30010aRehabilitation1 aNijboer, F.1 aSellers, E., W.1 aMellinger, J1 aJordan, M., A.1 aMatuz, T1 aFurdea, Adrian1 aHalder, S1 aMochty, U.1 aKrusienski, D., J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aKübler, A uhttp://www.ncbi.nlm.nih.gov/pubmed/1857198401649nas a2200241 4500008004100000022001400041245004600055210004500101260001200146300001200158490000800170520095000178653002901128653002901157653001701186653004201203100002301245700002001268700002601288700002401314700002101338856004801359 2008 eng d a0165-027000aToward enhanced P300 speller performance.0 aToward enhanced P300 speller performance c01/2008 a15–210 v1673 aThis study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol 1988;70:510-23]. Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.10abrain-computer interface10aevent related potentials10aP300 speller10astepwise linear discriminant analysis1 aKrusienski, D., J.1 aSellers, E., W.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1782277701471nas 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/1760568202016nas a2200265 4500008004100000022001400041245013800055210006900193260001200262300001400274490000700288520114400295653003401439653002901473653002501502653002901527653000901556653001901565100002201584700002501606700002601631700002401657700002101681856004801702 2006 eng d a0301-051100aA P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.0 aP300 eventrelated potential braincomputer interface BCI the effe c10/2006 a242–2520 v733 aWe describe a study designed to assess properties of a P300 brain-computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3 x 3 or 6 x 6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350 ms). Online accuracy was highest for the 3 x 3 matrix 175-ms ISI condition, while bit rate was highest for the 6 x 6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88%. P300 amplitude was significantly greater for the attended stimulus and for the 6 x 6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication.10aAmyotrophic Lateral Sclerosis10abrain-computer interface10aelectroencephalogram10aevent-related potentials10aP30010aRehabilitation1 aSellers, Eric, W.1 aKrusienski, Dean, J.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1686092003537nas 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/1558918402144nas a2200301 4500008004100000245008000041210006900121260001200190300001600202490000900218520113600227653002901363653002201392653002701414653002201441653003401463653002701497100002301524700001301547700002001560700001901580700002501599700002101624700001801645700001801663700003301681856012801714 2005 eng d00aRobust EEG Channel Selection across Subjects for Brain-Computer Interfaces.0 aRobust EEG Channel Selection across Subjects for BrainComputer I c01/2005 a3103–31120 v20053 a

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

10abrain-computer interface10achannel selection10aElectroencephalography10afeature selection10arecursive channel elimination10asupport vector machine1 aSchröder, Michael1 aLal, T N1 aHinterberger, T1 aBogdan, Martin1 aHill, Jeremy, Jeremy1 aBirbaumer, Niels1 aRosenstiel, W1 aSchölkopf, B1 aVesin J M, EbrahimiEditor, T uhttp://www.researchgate.net/publication/26532072_Robust_EEG_Channel_Selection_across_Subjects_for_Brain-Computer_Interfaces01351nas 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/1518887601629nas a2200265 4500008004100000022001400041245010300055210006900158260001200227300001200239490000800251520078600259653003101045653002901076653002801105653002701133653002401160653002001184653001901204100001901223700002601242700002601268700002101294856004801315 2003 eng d a0304-394000aElectroencephalographic(EEG)-based communication: EEG control versus system performance in humans.0 aElectroencephalographicEEGbased communication EEG control versus c07/2002 a89–920 v3453 aPeople can learn to control electroencephalographic (EEG) sensorimotor rhythm amplitude so as to move a cursor to select among choices on a computer screen. We explored the dependence of system performance on EEG control. Users moved the cursor to reach a target at one of four possible locations. EEG control was measured as the correlation (r(2)) between rhythm amplitude and target location. Performance was measured as accuracy (% of targets hit) and as information transfer rate (bits/trial). The relationship between EEG control and accuracy can be approximated by a linear function that is constant for all users. The results facilitate offline predictions of the effects on performance of using different EEG features or combinations of features to control cursor movement.10aaugmentative communication10abrain-computer interface10abrain-machine interface10aElectroencephalography10amu and beta rhythms10aneuroprosthesis10aRehabilitation1 aSheikh, Hesham1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1282117802517nas a2200241 4500008004100000022001400041245007400055210006900129260001200198300001600210490000800226520179400234653001302028653002902041653002502070653001902095653001902114100002302133700002602156700002402182700002102206856004802227 2003 eng d a1388-245700aEMG contamination of EEG: spectral and topographical characteristics.0 aEMG contamination of EEG spectral and topographical characterist c09/2003 a1580–15930 v1143 aOBJECTIVE: Electromyogram (EMG) contamination is often a problem in electroencephalogram (EEG) recording, particularly, for those applications such as EEG-based brain-computer interfaces that rely on automated measurements of EEG features. As an essential prelude to developing methods for recognizing and eliminating EMG contamination of EEG, this study defines the spectral and topographical characteristics of frontalis and temporalis muscle EMG over the entire scalp. It describes both average data and the range of individual differences. METHODS: In 25 healthy adults, signals from 64 scalp and 4 facial locations were recorded during relaxation and during defined (15, 30, or 70% of maximum) contractions of frontalis or temporalis muscles. RESULTS: In the average data, EMG had a broad frequency distribution from 0 to >200 Hz. Amplitude was greatest at 20-30 Hz frontally and 40-80 Hz temporally. Temporalis spectra also showed a smaller peak around 20 Hz. These spectral components attenuated and broadened centrally. Even with weak (15%) contraction, EMG was detectable (P<0.001) near the vertex at frequencies >12 Hz in the average data and >8 Hz in some individuals. CONCLUSIONS: Frontalis or temporalis muscle EMG recorded from the scalp has spectral and topographical features that vary substantially across individuals. EMG spectra often have peaks in the beta frequency range that resemble EEG beta peaks. SIGNIFICANCE: While EMG contamination is greatest at the periphery of the scalp near the active muscles, even weak contractions can produce EMG that obscures or mimics EEG alpha, mu, or beta rhythms over the entire scalp. Recognition and elimination of this contamination is likely to require recording from an appropriate set of peripheral scalp locations.10aartifact10abrain-computer interface10aelectroencephalogram10aelectromyogram10aRehabilitation1 aGoncharova, I., I.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1294878701315nas a2200289 4500008004100000022001400041245006100055210005800116260001200174300001600186490000800202520045900210653003100669653002900700653002700729653002000756653002900776653002800805653001400833653001900847653002400866100001900890700002100909700002600930700002100956856004800977 2000 eng d a1388-245700aEEG-based communication: presence of an error potential.0 aEEGbased communication presence of an error potential c12/2000 a2138–21440 v1113 aEEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect.10aaugmentative communication10abrain-computer interface10aElectroencephalography10aerror potential10aerror related negativity10aevent related potential10amu rhythm10aRehabilitation10asensorimotor cortex1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aPfurtscheller, G uhttp://www.ncbi.nlm.nih.gov/pubmed/11090763