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An Adaptive SSVEP-Based Brain-Computer Interface to Compensate Fatigue-Induced Decline of Performance in Practical

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    High-frequency steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) show promise for communication. Adaptive algorithms effectively compensate for performance decline due to eye fatigue, enabling robust BCI systems.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Human-Computer Interaction

    Background:

    • Steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) offer communication solutions for individuals with motor and speech impairments.
    • Eye fatigue from low-frequency stimuli degrades SSVEP BCI performance over time.
    • High-frequency stimuli (>25 Hz) can mitigate eye fatigue but may impact BCI performance.

    Purpose of the Study:

    • To quantitatively assess the performance decrease of high-frequency SSVEP BCIs over time.
    • To develop and evaluate adaptive algorithms for compensating performance degradation.
    • To establish a robust SSVEP BCI communication system for practical use.

    Main Methods:

    • Implementation of an asynchronous spelling system using a threshold-based LASSO algorithm for frequency recognition.
    • Conducting long online experiments with participants typing sentences using the BCI system.
    • Comparing fixed thresholds versus two proposed adaptive algorithms (tolerance-based and relational) to assess typing speed and accuracy.

    Main Results:

    • Fixed thresholds led to a significant decrease in typing speed over time across all subjects.
    • Proposed adaptive algorithms maintained consistent typing speed without significant degradation.
    • The adaptive relational algorithm achieved a higher average typing speed (8.7 char/min) compared to the tolerance-based algorithm (8.1 char/min) and fixed thresholds (>6 char/min).

    Conclusions:

    • Adaptive algorithms effectively compensate for fatigue-induced performance decline in high-frequency SSVEP BCIs.
    • The proposed relational algorithm offers a robust solution for practical BCI communication systems.
    • This research enhances the usability and reliability of SSVEP BCIs for individuals with disabilities.