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Improving EEG-Based Cross-Subject Mental Workload Classification Performance with Euclidean-Aligned Periodic and

Tao Wang, Yufeng Ke, Feng He

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    This study introduces a novel electroencephalography (EEG) model for mental workload (MWL) classification without user calibration. By using aligned periodic and aperiodic EEG features, it significantly improves cross-subject accuracy, reducing data collection burdens.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Cross-subject electroencephalography (EEG)-based mental workload (MWL) monitoring faces challenges due to performance decline in new users.
    • Traditional methods necessitate time-consuming and labor-intensive calibration data collection for each new subject.
    • Existing power spectral density (PSD) features often struggle with inter-subject variability.

    Purpose of the Study:

    • To develop a novel cross-subject MWL classification model that eliminates the need for calibration data.
    • To investigate the efficacy of periodic and aperiodic EEG components as features for MWL monitoring.
    • To enhance the generalizability and reduce the practical barriers of EEG-based MWL systems.

    Main Methods:

    • EEG data was decomposed into periodic and aperiodic components, replacing traditional PSD features.
    • A modified Euclidean alignment method was employed to align these novel features across subjects.
    • A cross-subject MWL classification model was built using the aligned features.

    Main Results:

    • The proposed model using aligned periodic and aperiodic features achieved a classification accuracy of 0.791±0.077.
    • This accuracy significantly outperformed raw PSD features without alignment (0.731±0.086, p<0.05).
    • A significant negative correlation (r=-0.472, p<0.001) was found between resting-state periodic feature distances and classification accuracy, suggesting potential for subject selection.

    Conclusions:

    • The novel approach using aligned periodic and aperiodic EEG features effectively enables calibration-free cross-subject MWL classification.
    • Leveraging resting-state data may allow for the selection of optimal source subjects to further improve target subject classification.
    • This method holds promise for developing more practical and widely applicable EEG-based MWL monitoring systems.