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A Multifocal SSVEPs-based Brain-Computer Interface with Less Calibration Time.

Jiabei Tang, Minpeng Xu, Zheng Liu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new brain-computer interface (BCI) coding method using multifocal steady-state visual evoked potentials (mfSSVEPs). This approach enables a large number of BCI instructions with significantly reduced training time and high accuracy.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Human-Computer Interaction

    Background:

    • Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) show significant progress.
    • Increasing the number of BCI instructions is crucial for broader applications.
    • Long calibration times hinder practical BCI usage with numerous instructions.

    Purpose of the Study:

    • To develop a novel coding method for a large set of BCI instructions.
    • To reduce the calibration time required for BCI systems.
    • To enhance the practicality and efficiency of EEG-based BCIs.

    Main Methods:

    • Proposed a new coding method using multifocal steady-state visual evoked potentials (mfSSVEPs).
    • Binary coded 16 targets using 4 distinct frequencies.
    • Required training data for only 5 out of 16 targets for calibration.
    • Employed task-related component analysis and a probabilistic model for target recognition.

    Main Results:

    • Achieved high accuracy of 93.1% with 1-second data length.
    • Reached a peak information transfer rate (ITR) of 101.1 bits/min.
    • Demonstrated an average ITR of 73.9 bits/min.
    • Validated the paradigm with five human volunteers.

    Conclusions:

    • The proposed mfSSVEP-based coding paradigm is effective for encoding a large BCI instruction set.
    • This method significantly reduces the necessary training data and calibration time.
    • The findings suggest a promising direction for practical and efficient BCI applications.