Abstract：Aiming at the problems of data redundancy and complex hardware facilities in the traditional fatigue detection method based on EEG, in this work, a channel selection method based on threshold filtering was proposed and used to select the optimal channel for several features by calculating the standard deviation and other metrics. Furthermore, hierarchical extreme learning machine (H-ELM) and PSO-H-ELM optimized by particle swarm optimization (PSO) were used to classify the selected optimal channel data and compared the accuracy to the results obtained by all the channels. The proposed method was validated using two groups of experimental data (one dataset was collected by a laboratory driving simulation device with 6 subjects and one dataset was a public dataset with 12 subjects and different collection devices). The results showed that for 18 subjects, many optimal channels were obtained from the ensemble empirical mode decomposition (EEMD) by using the power spectral density (PSD) feature of intrinsic mode function (IMF), and the distribution of optimal channels was roughly the same and the number of channels was small for the same device (8 and 11 channels, respectively). In addition, this channel selection method also significantly improved the accuracy of fatigue driving detection (the average accuracy of 18 subjects using the optimal channels was 99.75 %, 19.36 % higher than that using the full channel). Moreover, the ideal channel of sample entropy (SE) and the ideal channel of PSD hardly overlap, indicating that the two features were well complementary, and the practicality of proposed method is greatly improved by combining these 2 features. The results proved the effectiveness of the proposed method, which was of value in the application of fatigue driving detection.
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