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Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single Stimulus.

Tianyi Yan, Zhiyuan Ming, Yilun Huang

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |June 13, 2025
    PubMed
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    A novel spatial encoding-visually evoked potential (SE-VEP) brain-computer interface (BCI) reduces user fatigue and improves efficiency. This new SE-VEP model uses optimized target points for electroencephalogram (EEG) encoding, outperforming traditional SSVEP systems.

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Human-Computer Interaction

    Background:

    • Steady-state visually evoked potentials (SSVEPs) are efficient for brain-computer interfaces (BCIs) but cause visual fatigue and stimulus interference.
    • Limitations of traditional SSVEP methods necessitate innovative BCI paradigms for improved user experience and performance.

    Purpose of the Study:

    • To introduce and validate a novel spatial encoding-visually evoked potential (SE-VEP) BCI paradigm.
    • To address the limitations of traditional SSVEP BCIs, specifically visual fatigue and stimulus interference.
    • To optimize target point placement for efficient electroencephalogram (EEG) encoding within a single stimulus block.

    Main Methods:

    • Developed a SE-VEP model using four optimized target points for gaze restriction around a stimulus block.

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  • Employed a Riemann kernel-based support vector machine (R-SVM) for classifying electroencephalogram (EEG) data with varying eccentricities.
  • Validated the paradigm's feasibility through an online brain-controlled robotic virtual system and evaluated user fatigue and information transfer rate (ITR).

    • Main Results:

    • Achieved a classification accuracy of up to 86.11% using the R-SVM approach.
    • Determined an optimal time window length of 1.2 s for online BCIs based on ITR evaluation.
    • Demonstrated significant reductions in user fatigue (2.8 ± 0.5 vs. 4.1 ± 0.6) and improved stimulus block utilization (24.6 ± 2.3 vs. 8.2 ± 1.1 bits/min) compared to traditional BCI systems.

    Conclusions:

    • The proposed SE-VEP paradigm is effective for online BCI control, offering a viable alternative to traditional SSVEP systems.
    • The SE-VEP model successfully mitigates user fatigue and enhances the efficiency of BCI applications.
    • Optimized spatial encoding and classification methods contribute to improved BCI performance and user experience.