Abstract
A brain computer interface (BCI) utilizes signals derived from electroencephalography (EEG) to establish a connection between a person's state of mind and a computer based signal processing system that interprets the EEG signals. The choice of suitable features of the available EEG signals is crucial for good BCI communication. The optimal set of features is strongly dependent on the subjects and on the used experimental paradigm. Based upon EEG data of an existing BCI system, we present a wrapper method for the automated selection of features. The proposed method combines a genetic algorithm (GA) for the selection of feature with a support vector machine (SVM) for their evaluation. Applying this GA-SVM method to data of several subjects and two different experimental paradigms, we show that our approach leads to enhanced or even optimal classification accuracy.
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