@article {3086,
	title = {EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis.},
	journal = {Journal of neural engineering},
	volume = {9},
	year = {2012},
	month = {04/2012},
	pages = {026014},
	abstract = {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 \&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.},
	keywords = {User-Computer Interface},
	issn = {1741-2552},
	doi = {10.1088/1741-2560/9/2/026014},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/22350501},
	author = {Mak, Joseph N. and Dennis J. McFarland and Theresa M Vaughan and McCane, Lynn M. and Tsui, Phillippa Z. and Zeitlin, Debra J. and Sellers, Eric W. and Jonathan Wolpaw}
}
