In view of the nonlinear characteristics of electroencephalography (EEG) signals collected in the driving fatigue state recognition research and the issue that the recognition accuracy of the driving fatigue state recognition method based on EEG is still unsatisfactory, this paper proposes a driving fatigue recognition method based on sample entropy (SE) and kernel principal component analysis (KPCA), which combines the advantage of the high recognition accuracy of sample entropy and the advantages of KPCA in dimensionality reduction for nonlinear principal components and the strong non-linear processing capability. By using support vector machine (SVM) classifier, the proposed method (called SE_KPCA) is tested on the EEG data, and compared with those based on fuzzy entropy (FE), combination entropy (CE), three kinds of entropies including SE, FE and CE that merged with KPCA. Experiment results show that the method is effective.
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.
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