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    This study introduces a sparse group representation model (SGRM) to reduce the data needed for training motor imagery (MI) brain-computer interfaces (BCIs). The novel method improves efficiency by using intersubject information, making BCIs more practical.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Machine Learning

    Background:

    • Motor imagery (MI) based brain-computer interfaces (BCIs) require extensive electroencephalogram (EEG) data for classifier training.
    • The lengthy calibration process hinders user adoption of BCI systems.

    Purpose of the Study:

    • To propose a novel sparse group representation model (SGRM) to enhance the efficiency of MI-based BCIs.
    • To reduce the calibration burden by effectively utilizing intersubject information.

    Main Methods:

    • Feature extraction using common spatial pattern.
    • Construction of a composite dictionary matrix using target and other subjects' training samples.
    • Exploitation of within-group and group-wise sparse constraints for compact sample representation.

    Main Results:

    • The SGRM method significantly reduces the number of required training samples from the target subject.
    • Achieved superior classification performance with only 40 trials for model calibration (Averaged accuracy = 78.2%, Kappa = 0.57 and 77.7%, Kappa = 0.55 on two datasets).

    Conclusions:

    • The proposed SGRM method demonstrates promising potential for improving the practicality of MI-based BCIs.
    • Effective utilization of intersubject data alleviates the calibration burden and enhances BCI usability.