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Open AccessArticle

Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis

Department of Computer, Nanchang University, Nanchang 330029, China
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Entropy 2018, 20(9), 701; https://doi.org/10.3390/e20090701
Received: 29 July 2018 / Revised: 7 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
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. View Full-Text
Keywords: driving fatigue; sample entropy; kernel principal component analysis; support vector machine driving fatigue; sample entropy; kernel principal component analysis; support vector machine
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Ye, B.; Qiu, T.; Bai, X.; Liu, P. Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis. Entropy 2018, 20, 701.

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