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Abstract

A Brain Computer Interface (BCI) system allows the direct interpretation of brain activity patterns (e.g. EEG signals) by a computer. Typical BCI applications comprise spelling aids
or environmental control systems supporting paralyzed patients that have lost motor control
completely.
The design of an EEG based BCI system requires good answers for the problem of selecting
useful features during the performance of a mental task as well as for the problem of classifying
these features.
For the special case of choosing appropriate EEG channels from several available channels,
we propose the application of variants of the Support Vector Machine (SVM) for both
problems. Although these algorithms do not rely on prior knowledge they can provide more
accurate solutions than standard lter methods [1] for feature selection which usually incorporate
prior knowledge about neural activity patterns during the performed mental tasks.
For judging the importance of features we introduce a new relevance measure and apply it
to EEG channels. Although we base the relevance measure for this purpose on the previously
introduced algorithms, it does in general not depend on specic algorithms but can be derived
using arbitrary combinations of feature selectors and
classifiers.