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    This study introduces a novel hybrid brain-machine interface (hBMI) that fuses electroencephalographic (EEG) and electromyographic (EMG) signals for improved hand gesture recognition. The system shows promise for neuroprosthetics, enhancing control through context-aware analysis.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Brain-machine interfaces (BMIs) offer potential for assistive technologies.
    • Integrating electroencephalography (EEG) and electromyography (EMG) can improve signal robustness.
    • Context-aware processing is crucial for adaptive BMI performance.

    Purpose of the Study:

    • To develop a novel hierarchical graphical model-based context-aware hybrid BMI (hBMI).
    • To fuse electroencephalographic (EEG) and electromyographic (EMG) signals for enhanced hand gesture classification.
    • To evaluate the system's feasibility and context-aware capabilities for neuroprosthetic applications.

    Main Methods:

    • Utilized a hierarchical graphical model for probabilistic fusion of EEG and EMG data.
    • Collected experimental data from five hand gestures performed and imagined with both limbs.
    • Performed within-session and online across-session classification analyses.
    • Investigated context-awareness using a simulated probabilistic approach.

    Main Results:

    • Demonstrated the feasibility of the proposed hBMI system through classification analyses.
    • Achieved successful classification of hand gestures using fused EEG and EMG signals.
    • Showcased the context-aware capabilities of the model in simulated scenarios.

    Conclusions:

    • The developed hBMI system effectively integrates EEG and EMG for robust hand gesture recognition.
    • The context-aware approach enhances the adaptability and performance of the BMI.
    • This work has significant implications for the advancement of neurophysiologically-driven robotic prosthetics.