%0 Journal Article %J Journal of neural engineering %D 2012 %T EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis. %A Mak, Joseph N. %A Dennis J. McFarland %A Theresa M Vaughan %A McCane, Lynn M. %A Tsui, Phillippa Z. %A Zeitlin, Debra J. %A Sellers, Eric W. %A Jonathan Wolpaw %K User-Computer Interface %X The 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 >60% of the variance in copy-spelling performance (p < 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. %B Journal of neural engineering %V 9 %P 026014 %8 04/2012 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/22350501 %R 10.1088/1741-2560/9/2/026014