Applying Genetic Algorithm to EEG Signals for Feature Reduction in Mental Task Classification

Brain-Computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing EEG signals measured in different mental states. Therefore, choosing suitable features is demanded for a good BCI communication. In this regard, one of the points to be considered is feature vector dimensionality. We present a method of feature reduction using genetic algorithm as a wide search method and we choose 6 best frequency band powers of EEG, in order to speed up processing and meanwhile avoid classifier over fitting. As a result a vector of power spectrum of EEG frequency bands (alpha, beta, gamma, delta & theta) was found that reduces the dimension while giving almost the same correct classification rate.