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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Electroencephalography (EEG)-based motor imagery (MI) is a key brain-computer interface (BCI) paradigm.
    • Single-brain MI BCIs face limitations in accuracy and stability.
    • Multi-brain BCIs offer potential but existing methods underutilize inter-brain coupling features.

    Purpose of the Study:

    • To develop an advanced EEG-based multi-brain MI decoding method.
    • To effectively capture coupling relationship features among multiple brains.
    • To improve decoding accuracy using limited EEG data.

    Main Methods:

    • Proposed a novel method utilizing coupling feature extraction and few-shot learning.
    • Collected simultaneous EEG data from multiple participants performing the same task.
    • Compared the proposed method against traditional single-brain and multi-brain approaches.

    Main Results:

    • The proposed method achieved a 14.23% performance improvement over single-brain mode in a 10-shot, three-class decoding task.
    • Demonstrated effective utilization of coupling relationship features among multiple brains.
    • Showcased usability and effectiveness with limited available EEG data.

    Conclusions:

    • The developed EEG-based multi-brain MI decoding method significantly enhances BCI performance.
    • The approach effectively captures inter-brain coupling features, outperforming existing methods.
    • This method is particularly valuable in scenarios with limited EEG data availability.