Analysis of driving fatigue detection based on fuzzy entropy of EEG signals

doi:10.16265/j.cnki.issn1003-3033.2018.04.003

Abstract

Abstract: To prevent traffic accidents caused by driver fatigue, this study was aimed at developing a driving fatigue detection method based on EEG signal and fuzzy entropy. Firstly, EEG signals during simulated normal driving and simulated fatigue driving were acquired from 28 testees. Secondly, the FE values were calculated based on the EEG signals of two driving states. Then four classifiers (including random forest(RF), support vector machine(SVM), decision tree(DT)and K-nearest neighbor(KNN)) were employed for detecting fatigue state. Finally, multiple performance indicators and ROC curve were adopted to analyze and compare the performance of driver fatigue detection. The results show that the FE value of fatigue driving based on EEG was significantly higher than that of normal driving, all four classifiers can detect driver fatigue state effectively, and K-nearest neighbor classifier achieves the optimal accuracy of 97.4%, and that the driver fatigue detection method based on fuzzy entropy of EEG signals has good robustness and stability.

Key words: electroencephalogram (EEG), fuzzy entropy (FE), driving fatigue, receiver operating characteristic (ROC) curve, classifie

CLC Number:

X910

HU Jianfeng, WANG Taotao. Analysis of driving fatigue detection based on fuzzy entropy of EEG signals[J]. China Safety Science Journal, 2018, 28(4): 13-18.

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