We present a systematic and straightforward approach to the problem of
single-trial classification of event-related potentials (ERP) in EEG.
Instead of using a generic classifier off-the-shelf, like a neural
network or support vector machine, our classifier design is guided by
prior knowledge about the problem and statistical properties found in
the data. In particular, we exploit the well-known fact that
event-related drifts in EEG potentials, albeit hard to detect in a
single trial, 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 and unlabeled data. The
method is successfully applied to a data set from the NIPS*2001
Brain–Computer Interface post-workshop competition. Our result
turned out to be competitive with the best result of the competition.
