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J Neural Eng. 2012 Apr;9(2):026014. doi: 10.1088/1741-2560/9/2/026014. Epub 2012 Feb 21.

EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis.

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1
NeuroSky (Hong Kong) Co. Ltd, Hong Kong, People's Republic of China. josephnfmak@gmail.com

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 >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.

PMID:
22350501
DOI:
10.1088/1741-2560/9/2/026014
[Indexed for MEDLINE]
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