Abstract
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.
Highlights
Driving fatigue is a phenomenon in which, due to continuous driving, drivers’ ability of perception, judgment and operation appear to decrease [1]
Guo et al [3] explored the correlation between ECG indicators and driving fatigue state based on ECG signals and constructed the driving fatigue state recognition model combined with the support vector machine (SVM) classifier
The specific test was performed under three different principal component contribution rates by using the kernel principal component analysis (KPCA) method of the P-order polynomial kernel function, radial basis function and multilayer perceptual kernel function, and every optimal parameters was obtained through multiple experiments
Summary
Driving fatigue is a phenomenon in which, due to continuous driving, drivers’ ability of perception, judgment and operation appear to decrease [1]. Zhao et al [6] constructed a driving fatigue recognition model based on the human eye feature by using a concatenated convolutional neural network. Chai and Naik et al [8] used entropy rate bound minimization as a source separation technique, the autoregressive (AR) modeling as the feature extraction algorithm and the Bayesian neural network as the classification algorithm for driving fatigue recognition; they combined independent component by entropy rate bound minimization analysis. The recognition accuracy of the driving fatigue state obtained by using these methods is still not satisfactory after the feature extraction of EEG signals. This paper proposes a driving fatigue recognition method based on sample entropy and kernel principal component analysis. Algorithm to achieve effective recognition of the driver’s fatigue state
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