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    This study introduces a new transfer learning method for steady-state visual-evoked potential (SSVEP) brain-computer interfaces (BCIs). The approach significantly improves recognition performance using minimal calibration data, enhancing BCI practicality.

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    Area of Science:

    • Neuroscience
    • Computer Science
    • Biomedical Engineering

    Background:

    • Training-based algorithms excel in SSVEP-BCIs but require extensive calibration data.
    • Calibration demands limit BCI practicality due to user fatigue and cost.
    • Existing transfer learning methods often need substantial source or target domain data.

    Purpose of the Study:

    • To introduce cross-dataset transfer learning for SSVEP BCIs.
    • To address the data mismatch problem in cross-dataset transfer learning.
    • To develop a practical SSVEP decoding algorithm with minimal calibration.

    Main Methods:

    • Proposed a novel cross-dataset transfer learning approach for SSVEP.
    • Introduced TL-CSTD (Transfer Learning for SSVEP decoding calibrated with Single-Trial data).
    • Utilized 2s of single-trial calibration data for template matching and knowledge extraction.

    Main Results:

    • TL-CSTD effectively overcomes the data mismatch problem.
    • Achieved excellent SSVEP recognition performance with only 2s of calibration data.
    • Demonstrated effectiveness across three large SSVEP datasets.

    Conclusions:

    • TL-CSTD significantly enhances the practicality and application potential of SSVEP-BCIs.
    • The method reduces the need for extensive user training and calibration.
    • This approach offers a viable solution for efficient and user-friendly BCI systems.