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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xin Xu & Jie Tang & Tingting Xu & Maokun Lin, 2023. "Mental Fatigue Degree Recognition Based on Relative Band Power and Fuzzy Entropy of EEG," IJERPH, MDPI, vol. 20(2), pages 1-13, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1447-:d:1034334
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    References listed on IDEAS

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    1. Jianfeng Wu & Lingyan Zhang & Hongchun Yang & Chunfu Lu & Lu Jiang & Yuyun Chen, 2022. "The Effect of Music Tempo on Fatigue Perception at Different Exercise Intensities," IJERPH, MDPI, vol. 19(7), pages 1-18, March.
    2. Azarnoosh, Mahdi & Motie Nasrabadi, Ali & Mohammadi, Mohammad Reza & Firoozabadi, Mohammad, 2011. "Investigation of mental fatigue through EEG signal processing based on nonlinear analysis: Symbolic dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 44(12), pages 1054-1062.
    3. Eglė Butkevičiūtė & Aleksėjus Michalkovič & Liepa Bikulčienė, 2022. "ECG Signal Features Classification for the Mental Fatigue Recognition," Mathematics, MDPI, vol. 10(18), pages 1-18, September.
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