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Motor Imagery Decoding from EEG under Visual Distraction via Feature Map Attention EEGNet.

Yiting Geng, Banghua Yang, Sixiong Ke

    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
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    Summary

    Investigating motor imagery (MI) brain-computer interfaces (BCI), this study explores visual distraction

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    Area of Science:

    • Neuroscience
    • Human-Computer Interaction
    • Rehabilitation Engineering

    Background:

    • Motor imagery (MI)-based brain-computer interfaces (BCI) are crucial for human-computer interaction and rehabilitation.
    • Existing research on electroencephalogram (EEG) signal decoding for MI often overlooks the impact of distractions, particularly visual ones, which are common in real-world scenarios.
    • Understanding how visual distractions affect MI performance is essential for developing more robust and practical BCI systems.

    Purpose of the Study:

    • To investigate the impact of visual distraction on motor imagery decoding performance using EEG signals.
    • To propose and evaluate a novel decoding method for MI under visual distraction.
    • To compare the proposed method's effectiveness against existing techniques.

    Main Methods:

    • A novel MI paradigm was designed incorporating visual distraction.
    • Distinct patterns of event-related desynchronization (ERD) and event-related synchronization (ERS) were analyzed under visual distraction.
    • A feature map attention EEGNet (FMA-EEGNet) was proposed for robust MI decoding from EEG signals.
    • The decoding performance of FMA-EEGNet was compared with four other methods using EEG data collected with and without visual distraction.

    Main Results:

    • The proposed FMA-EEGNet achieved a mean accuracy of 89.1% without visual distraction and 82.2% with visual distraction.
    • FMA-EEGNet demonstrated superior performance compared to other evaluated methods.
    • The proposed method showed minimal performance degradation even under visual distraction.

    Conclusions:

    • Visual distraction significantly impacts MI decoding performance.
    • The FMA-EEGNet model offers a robust solution for decoding MI signals in the presence of visual distraction.
    • This research advances the practical applicability of MI-BCI technology in real-world environments.