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Bayesian classification of single-trial event-related potentials in EEG

: Kohlmorgen, J.; Blankertz, B.


International Journal of Bifurcation and Chaos in Applied Sciences and Engineering 14 (2004), No.2, pp.719-726
ISSN: 0218-1274
Journal Article
Fraunhofer FIRST ()

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.