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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    SeU-net extends short steady-state visual evoked potential (SSVEP) signals without subject calibration. This novel method enhances brain-computer interface (BCI) performance, enabling faster and more accurate decoding for patients.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer significant patient benefits.
    • Current SSVEP recognition methods struggle with short signal lengths, limiting BCI performance.
    • Signal length is a critical factor for SSVEP recognition accuracy.

    Purpose of the Study:

    • To introduce SeU-net, a novel signal extension method for improving calibration-free SSVEP recognition on short signals.
    • To enhance the performance of SSVEP-BCIs by addressing the challenge of limited signal duration.
    • To develop a method that does not require subject-specific calibration data.

    Main Methods:

    • Proposed SeU-net employs Long Short-Term Memory (LSTM) and contrastive learning for feature extraction.
    • The method converts signals to feature space and back to sample space for signal extension.
    • SeU-net focuses on temporal domain signal extension, ensuring cross-subject applicability.

    Main Results:

    • SeU-net significantly improves the decoding performance of calibration-free methods for short SSVEP signals.
    • The method demonstrates strong cross-subject signal extension capabilities.
    • Experiments confirm enhanced accuracy with shorter SSVEP signal durations.

    Conclusions:

    • SeU-net effectively extends short SSVEP signals, boosting calibration-free BCI performance.
    • The method holds potential for advancing high-speed SSVEP-BCI applications.
    • Improved decoding accuracy with shorter signals broadens BCI accessibility and usability.