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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Rehabilitation Robotics

    Background:

    • Wearable robotic systems offer potential for musculoskeletal disorder patients.
    • Reliable and intuitive control is crucial for practical application of these devices.
    • Detecting user motion intention is a key challenge in human-robot interaction.

    Purpose of the Study:

    • To comprehensively evaluate electroencephalography (EEG) and electromyography (EMG) signal fusion methods for motion intention detection.
    • To assess the performance of EEG/EMG fusion under varying motion parameters like speed, weight, and muscle fatigue.
    • To provide evidence for the efficacy of EEG/EMG fusion in controlling wearable robotic devices.

    Main Methods:

    • Investigated various EEG/EMG signal fusion techniques for classifying motion intention.
    • Evaluated system performance during elbow flexion-extension tasks.
    • Varied parameters including motion speed, external weight, and induced muscle fatigue.

    Main Results:

    • EEG/EMG fusion did not significantly outperform EMG-only control (86.81 ± 3.98%).
    • Some fusion methods demonstrated equivalent performance to EMG alone (p=1.000).
    • EEG/EMG fusion exhibited reduced sensitivity to changes in motion parameters, ensuring more consistent performance.

    Conclusions:

    • EEG/EMG fusion provides a robust control strategy for wearable robots.
    • The enhanced consistency of EEG/EMG fusion across different conditions justifies its use.
    • This research supports the integration of EEG/EMG fusion for intuitive control of assistive robotic devices.