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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Human-Computer Interaction

    Background:

    • Event-related potentials (ERPs) are crucial for understanding cognitive processes and form the basis of brain-computer interfaces (BCIs).
    • P300 spellers, a type of ERP-based BCI, aid individuals with severe motor impairments in communication by decoding electroencephalography (EEG) signals.
    • Current P300 spellers often rely on conventional flash stimuli, which may have limitations in performance and user experience.

    Purpose of the Study:

    • To introduce a novel speller paradigm utilizing a dynamically growing bubble (GB) visualization as a stimulus, contrasting it with the traditional flash (TF) stimulus.
    • To propose a "Lock a Target by Two Flashes" (LT2F) method for more flexible stimulus presentation, alongside row/column (RC) and single character (SC) modes.
    • To evaluate the performance of a multi-windowed EEGNet (mwEEGNet) with a "Sub and Global" approach and compare it with eight other algorithms for enhanced EEG signal classification.

    Main Methods:

    • Employed a novel dynamically growing bubble (GB) stimulus paradigm and a "Lock a Target by Two Flashes" (LT2F) method.
    • Utilized a "Sub and Global" multi-windowed EEGNet (mwEEGNet) for enhanced EEG signal classification.
    • Compared the GB paradigm and mwEEGNet against the traditional flash (TF) stimulus and eight other representative algorithms in experiments with 20 healthy volunteers.

    Main Results:

    • The GB stimulus pattern evoked more pronounced negative peaks in parietal and occipital regions compared to the TF pattern.
    • The GB pattern, using mwEEGNet, achieved a 2.00% increase in online character accuracy (ACC) and a 5.39 bits/min improvement in information transfer rate (ITR) over the TF pattern.
    • mwEEGNet demonstrated superior classification performance compared to the other eight evaluated algorithms.

    Conclusions:

    • The proposed GB stimulus and mwEEGNet significantly advance the performance of ERP-based BCIs.
    • This novel approach offers a promising avenue for improving communication assistive technologies for individuals with motor disabilities.
    • The findings highlight the potential of innovative visual stimuli and advanced deep learning models in BCI research.