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    Deep learning models significantly enhance online motor imagery brain-computer interface performance. The IFNet model shows superior accuracy and cross-session learning compared to traditional methods, suggesting potential for clinical applications.

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    Area of Science:

    • Neuroscience
    • Machine Learning
    • Biomedical Engineering

    Background:

    • Deep learning models outperform traditional machine learning for offline motor imagery (MI) decoding from electroencephalogram (EEG).
    • Online MI-based brain-computer interfaces (BCIs) predominantly use machine learning decoders, often with suboptimal performance.
    • The effectiveness of deep learning for online EEG decoding in real-world BCI systems is not well-established.

    Purpose of the Study:

    • To evaluate the performance of a novel deep learning model, the interactive frequency convolutional neural network (IFNet), for online MI decoding.
    • To compare IFNet against the established filter-bank common spatial pattern (FBCSP) algorithm in a randomized online MI-BCI study.
    • To investigate the generalization capabilities and cross-session learning effects of deep learning in online BCI applications.

    Main Methods:

    • A randomized, cross-session online MI-BCI study was conducted with 15 BCI-naive subjects performing 2D center-out tasks.
    • The proposed IFNet deep learning model was implemented and compared directly with the FBCSP algorithm for online MI decoding.
    • Performance metrics were analyzed across multiple sessions to assess accuracy, learning effects, and generalizability.

    Main Results:

    • The IFNet deep learning decoder consistently outperformed the FBCSP algorithm in online MI decoding across various metrics.
    • IFNet demonstrated significant improvements in average online task accuracy, increasing it by 20% and 27% over two sessions compared to FBCSP.
    • A significant cross-session training effect was observed with IFNet (P=0.017), unlike the FBCSP method (P=0.337).
    • Offline evaluations confirmed IFNet's superior performance against other state-of-the-art deep learning models.

    Conclusions:

    • This study provides early evidence for the substantial enhancement of online MI-BCI performance using deep learning.
    • The IFNet model shows significant advantages in online decoding accuracy and adaptive learning capabilities.
    • The findings suggest deep learning, particularly IFNet, holds considerable promise for advancing MI-BCIs and their clinical utility, such as in stroke rehabilitation.