An Algorithm for Extracting Entropy Features from EEG Signals Based on T-test and KPCA and Its Application on Driving Fatigue State Recognition

Abstract

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.

A New Feature Analysis Approach to Selecting Channels of EEG for Fatigue Driving

“…At the same time, by using the algorithm in this paper and the algorithm in other papers for fatigue driving status recognition experiments under the same data set, it is concluded that the proposed method in this paper has reduced the number of channels and improved the accuracy, which [25] 30 99.40% SE_T_KPCA [26] 30 99.27% CSPT_FBN [27] 7 99.17% Adaptive multiscale FE [28] 2 (FP1, FP2) 95.37% Multiscale FE based on the EMD [29] 2 (FP1, FP2) 87.50% fuzzy entropy features. Figure 6 shows the brain map of each subject based on the fusion features.…”

“…In the experiments of this paper, firstly, the sampling frequency of EEG signal was reduced to 128 Hz, secondly, a sixlayer wavelet packet decomposition tree was built, and then the original EEG signal was decomposed into four subbands, including Theta subband (4-8 Hz), Alpha subband (8-13 Hz), Beta1 subband (13)(14)(15)(16)(17)(18)(19)(20), and Beta2 subband (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Finally, the standard deviation (Std) features are extracted for each subband, and the best performing subband features are fused with the fuzzy entropy features to form the fused features.…”

“…The names of the electrodes correspond to their positions as follows: Fp1 (1), Fp2 (2), F7 (3), F3 (4), Fz (5), F4 (6), F8 (7), FT7 (8), FC3 (9), FCz (10), FC4 (11), FT8 (12), T3 (13), C3 (14), Cz (15), C4 (16), T4 (17), TP7 (18), CP3 (19), CPz (20), CP4 (21), TP8 (22), T5 (23), P3 (24), Pz (25), P4 (26), T6 (27), O1 (28), Oz (29), and O2 (30).…”

A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model

“…It is worth noting that in the applied research in the biomedical field, Wakeling and Rozitis (2004) further explored the relationship between sEMG frequency and motor unit type based on the PCA method. Zou et al (2020) completed EEG entropy feature extraction based on the fusion method of T-test and KPCA, and realized the identification of drivers’ fatigue driving state. Zhao et al (2020a) simplified the feature matrix by combining PCA and ICA methods to achieve effective feature extraction and classification tasks of ECG signals.…”