Single Trial Detection of EEG Error Potentials: A Tool for Increasing BCI Transmission Rates
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
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In this paper we present a simple and straightforward approach to the problem of single-trial classification of event-related potentials (ERP) in EEG. We exploit the well-known fact that event-related drifts in EEG potentials can well be observed if averaged over a sufficiently large number of trials. We propose to use the average signal and its variance as a generative model for each event class and use Bayes decision rule for the classification of new, unlabeled data. The method is successfully applied to a data set from the NIPS*2001 Brain-Computer Interface post-workshop competition.