Mental Fatigue Degree Recognition Based on Relative Band Power and Fuzzy Entropy of EEG

Xin Xu (), Jie Tang, Tingting Xu and Maokun Lin
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Xin Xu: School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Jie Tang: School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Tingting Xu: School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Maokun Lin: School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

IJERPH, 2023, vol. 20, issue 2, 1-13

Abstract: Mental fatigue is a common phenomenon in our daily lives. Long-term fatigue can lead to a decline in a person’s operational functions and seriously affect work efficiency. In this paper, a method that recognizes the degree of mental fatigue based on relative band power and fuzzy entropy of Electroencephalogram (EEG) is proposed. The N-back experiment was used to induce mental fatigue in subjects, and the corresponding EEG signals were recorded during the experiment. A preprocessing method based on complementary ensemble empirical modal decomposition (CEEMD) and independent component analysis (ICA) was designed to remove noise from the raw EEG signal. The relative band power feature, which has been used extensively in fatigue recognition studies, was extracted from the EEG signals. Meanwhile, fuzzy entropy, a feature commonly used in attention recognition, was also extracted for fatigue recognition, based on previous findings that an increase in fatigue is accompanied by a decrease in attention. The two features were fed into an extreme gradient boosting (XGBoost) classifier to distinguish three different degrees of fatigue, which resulted in an average accuracy of 92.39% based on data from eight subjects. The promising results indicate the effectiveness of the proposed method in mental fatigue degree identification.

Keywords: mental fatigue; EEG; N-back task; CEEMD; ICA; feature extraction; ensemble learning; XGBoost (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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