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Decoding Natural Grasping Behaviors: Insights Into MRCP Source Features and Coupling Dynamics.

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    Summary

    Movement-related cortical potential (MRCP) source features effectively decode natural grasping actions, distinguishing subtle hand movements. This research advances brain-computer interface control for neural prosthetics.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Human Motor Control

    Background:

    • Decoding natural grasping is key for intuitive neural prosthetic control.
    • Understanding cortical-muscle interactions during grasping is essential for advanced brain-computer interfaces (BCIs).

    Purpose of the Study:

    • To investigate the decoding performance of movement-related cortical potential (MRCP) source features for natural grasping actions.
    • To explore temporal and frequency differences in cortical-muscular coupling during grasping movements.
    • To enhance the natural and intuitive control of BCIs for grasping tasks.

    Main Methods:

    • Collected 64-channel electroencephalogram (EEG) and 5-channel surface electromyogram (sEMG) data from 17 healthy participants.
    • Analyzed five natural grasping motions (medium wrap, adducted thumb, adduction grip, tip pinch, writing tripod).
    • Projected six EEG frequency bands into source space for detailed analysis of MRCP features and coupling strengths.

    Main Results:

    • MRCP source features successfully distinguished between power and precision grasps, and identified subtle thumb movements.
    • Cortical-to-muscle coupling strength was generally lower than muscle-to-cortical coupling during reach-and-grasp, except in the hold phase (γ frequency).
    • A distinct 12-Hz peak in inter-muscular coupling strength was observed during movement execution, potentially linked to motor planning and execution.

    Conclusions:

    • MRCP source features offer robust decoding of complex grasping behaviors.
    • Findings reveal distinct temporal and frequency dynamics in cortical-muscular interactions during natural grasping.
    • This research provides insights into human hand grasping control, paving the way for more natural BCI applications.