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    This study shows how the brain adapts when learning to use head-controlled assistive devices, improving movement and identifying brainwave patterns (EEG biomarkers) that signal learning progress.

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    Area of Science:

    • Neuroscience
    • Rehabilitation Engineering
    • Human-Computer Interaction

    Background:

    • Limited research exists on cognitive-motor learning for upper-limb assistive devices.
    • Previous studies focused on performance, not brain activity (cerebral cortical dynamics).
    • Electroencephalography (EEG) biomarkers, specifically beta spectral power, may indicate learning and memory formation.

    Purpose of the Study:

    • To investigate how sensorimotor performance and EEG beta spectral power change during practice with a head-controlled robotic interface.
    • To identify brain biomarkers associated with learning novel sensorimotor mappings.

    Main Methods:

    • Individuals without disabilities practiced reaching movements using a simulated robotic effector controlled by a head interface.
    • EEG data, including low- and high-beta spectral power, were recorded during practice.
    • Sensorimotor performance metrics (e.g., movement speed, straightness) were analyzed.

    Main Results:

    • Participants demonstrated faster and straighter reaching movements with practice.
    • Low-beta power increased in frontal, central, and parietal regions during movement planning.
    • High-beta power decreased in the temporal region as practice progressed.

    Conclusions:

    • Observed spectral modulations (changes in brainwaves) may represent the encoding of sensorimotor mapping within an internal model.
    • These EEG biomarkers can potentially monitor learning and cognitive-motor adaptation during rehabilitation with assistive devices.