Feature Selection Based on Fisher Ratio and Mutual Information Analyses for Robust Brain Computer Interface

Abstract

This paper proposes a novel feature selection method based on two-stage analysis of Fisher ratio and mutual information for robust brain computer interface. This method decomposes multichannel brain signals into subbands. The spatial filtering and feature extraction is then processed in each subband. The two-stage analysis of Fisher ratio and mutual information is carried out in the feature domain to reject the noisy feature indexes and select the most informative combination from the remaining. In the approach, we develop two practical solutions, avoiding the difficulties of using high dimensional mutual information in the application, that are the feature indexes clustering using cross mutual information and the latter estimation based on conditional empirical PDF. We test the proposed feature selection method on two BCI data sets and the results are at least comparable to the best results in the literature. The main advantage of proposed method is that the method is free from any time-consuming parameter tweaking and therefore suitable for the BCI system design.