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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Asynchronous control in brain-computer interface (BCI) systems necessitates rapid brain switches with minimal false positive rates (FPR).
    • Existing BCI technologies face challenges in achieving both speed and accuracy for seamless user interaction.
    • Code-modulated visual-evoked potentials (c-VEP) offer a promising avenue for robust neural signal modulation.

    Purpose of the Study:

    • To develop and validate a novel brain switch for asynchronous BCI control utilizing c-VEP.
    • To optimize the c-VEP modulation and decoding strategy for enhanced performance.
    • To evaluate the reaction time (RT) and false positive rate (FPR) of the proposed brain switch.

    Main Methods:

    • Implementation of a brain switch system based on seven 8-bit pseudorandom codes to modulate electroencephalogram (EEG) signals.
    • Optimization and demonstration of the control strategy through both offline and online experimental paradigms.
    • Continuous decoding of brain states using the task-related component analysis (TRCA) algorithm.

    Main Results:

    • The c-VEP brain switch achieved an average reaction time (RT) of 1.72 seconds in the online experiment.
    • The system demonstrated an average idle time of 183.53 seconds.
    • Crucially, no false positive events were recorded during the online experiment, indicating high reliability.

    Conclusions:

    • The proposed c-VEP-based brain switch provides a fast and reliable solution for asynchronous BCI control.
    • The TRCA algorithm effectively decodes brain states, enabling efficient BCI operation.
    • This approach significantly advances the potential for practical and user-friendly BCI applications.