An Algorithm for Extracting Entropy Features from EEG Signals Based on T-test and KPCA and Its Application on Driving Fatigue State Recognition

Abstract

In consideration of the nonlinear characteristics of electroencephalography (EEG) signals collected in the research on driving fatigue state recognition, the recognition accuracy and the time performance of the driving fatigue state recognition method based on EEG is still not ideal, we construct a driving fatigue state recognition model and corresponding recognition method by combining t-test with kernel principal component analysis based on EEG entropy features. By applying this method to 30-electrode EEG data, testing it with 7 kinds of classifiers and comparing the results with the results without t-test, we find that the proposed method not only improve time performance, but also has the ideal accuracy. Through selecting the best classifier, the recognition accuracy and time performance are improved.