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.