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Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment

Author

Listed:
  • Hongquan Qu

    (School of Information Science and Technology, North China University of Technology, Beijing 100144, China)

  • Mengyu Zhang

    (School of Information Science and Technology, North China University of Technology, Beijing 100144, China)

  • Liping Pang

    (School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China)

Abstract

Electroencephalogram (EEG) signals are sensitive to the level of Mental Workload (MW). However, the random non-stationarity of EEG signals will lead to low accuracy and a poor generalization ability for cross-session MW classification. To solve this problem of the different marginal distribution of EEG signals in different time periods, an MW classification method based on EEG Cross-Session Subspace Alignment (CSSA) is presented to identify the level of MW induced in visual manipulation tasks. The Independent Component Analysis (ICA) method is used to obtain the Independent Components (ICs) of labeled and unlabeled EEG signals. The energy features of ICs are extracted as source domains and target domains, respectively. The marginal distributions of source subspace base vectors are aligned with the target subspace base vectors based on the linear mapping. The Kullback–Leibler (KL) divergences between the two domains are calculated to select approximately similar transformed base vectors of source subspace. The energy features in all selected vectors are trained to build a new classifier using the Support Vector Machine (SVM). Then it can realize MW classification using the cross-session EEG signals, and has good classification accuracy.

Suggested Citation

  • Hongquan Qu & Mengyu Zhang & Liping Pang, 2022. "Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment," Mathematics, MDPI, vol. 10(11), pages 1-14, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1875-:d:827917
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    References listed on IDEAS

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    1. Bozhokin, S.V. & Suslova, I.B., 2015. "Wavelet-based analysis of spectral rearrangements of EEG patterns and of non-stationary correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 151-160.
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