02062nas a2200241   4500008004100000022001400041245012200055210006900177260001200246300001100258490000600269520128800275653002801563100002001591700002601611700002401637700002101661700002401682700002301706700002201729700002101751856004801772       2012                        eng d  a1741-255200aEEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis.0 aEEG correlates of P300based braincomputer interface BCI performa  c04/2012  a0260140 v93 aThe purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for &gt;60% of the variance in copy-spelling performance (p &lt; 0.001, mean R(2)?= 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.10aUser-Computer Interface1 aMak, Joseph, N.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aMcCane, Lynn, M.1 aTsui, Phillippa, Z.1 aZeitlin, Debra, J.1 aSellers, Eric, W.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2235050101801nas a2200181   4500008004100000022001400041245006700055210006200122260001200184300001400196490000700210520125900217653002801476100002201504700002401526700002101550856004801571       2010                        eng d  a1471-180X00aA brain-computer interface for long-term independent home use.0 abraincomputer interface for longterm independent home use  c10/2010  a449–4550 v113 aOur objective was to develop and validate a new brain-computer interface (BCI) system suitable for long-term independent home use by people with severe motor disabilities. The BCI was used by a 51-year-old male with ALS who could no longer use conventional assistive devices. Caregivers learned to place the electrode cap, add electrode gel, and turn on the BCI. After calibration, the system allowed the user to communicate via EEG. Re-calibration was performed remotely (via the internet), and BCI accuracy assessed in periodic tests. Reports of BCI usefulness by the user and the family were also recorded. Results showed that BCI accuracy remained at 83% (r = -.07, n.s.) for over 2.5 years (1.4% expected by chance). The BCI user and his family state that the BCI had restored his independence in social interactions and at work. He uses the BCI to run his NIH-funded research laboratory and to communicate via e-mail with family, friends, and colleagues. In addition to this first user, several other similarly disabled people are now using the BCI in their daily lives. In conclusion, long-term independent home use of this BCI system is practical for severely disabled people, and can contribute significantly to quality of life and productivity.10aUser-Computer Interface1 aSellers, Eric, W.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2058394701626nas a2200229   4500008004100000022001400041245006800055210006500123260001200188300001400200490000600214520094100220653002401161100002501185700002201210700002601232700001901258700002601277700002401303700002101327856004801348       2006                        eng d  a1741-256000aA comparison of classification techniques for the P300 Speller.0 acomparison of classification techniques for the P300 Speller  c12/2006  a299–3050 v33 aThis study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.10aNormal Distribution1 aKrusienski, Dean, J.1 aSellers, Eric, W.1 aCabestaing, François1 aBayoudh, Sabri1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1712433402016nas 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/16860920