Comparison of decision rules for automatic EEG classification

Abstract

Discusses eight classification rules, four based on parametric Gaussian assumptions and four based on nonparametric k-nearest neighbor density estimation, which were tested on human EEG samples representing seven forms of mental activity. With a set of primary EEG features, the k-NN rules, as a class, were significantly more effective than the parametric classifiers; best results were obtained with an optimized version of the generalized k-NN rule. With a reduced set of secondary features, the two types performed approximately equally, but below the best k-NN performances in the original space.