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    A novel dual-frequency steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) paradigm effectively suppresses interference, achieving high information transmission rates for training-free applications. This advancement significantly boosts BCI performance and potential for large target numbers.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Human-Computer Interaction

    Background:

    • Current brain-computer interface (BCI) paradigms using steady-state visual evoked potential (SSVEP) often suffer from unpredictable intermodulation components, limiting their efficiency.
    • The development of training-free BCI systems is crucial for widespread and practical applications.
    • Dual-frequency SSVEP paradigms offer a promising approach but require further optimization to enhance signal quality and reduce interference.

    Purpose of the Study:

    • To introduce and validate a novel dual-frequency modulated SSVEP BCI paradigm designed to suppress intermodulation components.
    • To evaluate the performance of this new paradigm in both offline and online, training-free BCI scenarios.
    • To demonstrate the suitability of the paradigm for achieving a large number of targets in BCI systems.

    Main Methods:

    • A dual-frequency binocular vision SSVEP BCI system was developed utilizing circularly polarized light technology.
    • Two experiments were conducted: a 6-target offline experiment and a 40-target online experiment.
    • An improved filter bank dual-frequency canonical correlation analysis (FBDCCA) algorithm was employed for data processing.

    Main Results:

    • Energy analysis in the offline experiment showed a 2 dB increase in the signal-to-noise ratio for target frequency components compared to intermodulation components.
    • The online training-free experiment achieved an average information transmission rate (ITR) of 104.56 ± 15.74 bits/min, nearly double the performance of existing dual-frequency paradigms.
    • The average information transfer rate for offline training analysis reached 180.87 ± 17.88 bits/min.

    Conclusions:

    • The proposed dual-frequency SSVEP BCI paradigm effectively suppresses intermodulation harmonics and enhances response quality.
    • The paradigm demonstrates high ITR in both training and training-free conditions, proving its versatility.
    • This novel paradigm is well-suited for large-scale target BCI systems and offers significant potential for future applications.