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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Rehabilitation Technology

    Background:

    • Brain-computer interface (BCI) systems face challenges in clinical translation due to limited force feedback and system longevity.
    • The subthalamic nucleus (STN) is a potential target for decoding motor intent for prosthetic control.

    Purpose of the Study:

    • To investigate algorithms for decoding continuous force signals from the STN.
    • To identify an optimal algorithm for real-time, continuous force decoding in BCIs.

    Main Methods:

    • Exploration of various algorithms including Wiener filter, Wiener-Cascade model, Kalman filter, and dynamic neural networks.
    • Real-time decoding of continuous force signals from STN recordings.

    Main Results:

    • The Wiener-Cascade model demonstrated superior performance for decoding force from the STN.
    • Successful real-time decoding of continuous force signals was achieved.
    • Low latency decoding of more than two brain states is possible.

    Conclusions:

    • The Wiener-Cascade model is recommended for decoding force from the STN in BCI applications.
    • Real-time force decoding from the STN enhances the potential for advanced prosthetic control.
    • This approach addresses key limitations hindering clinical translation of BCI systems.