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A Stimulus Artifact Removal Technique for SEMG Signal Processing During Functional Electrical Stimulation.

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    |March 3, 2015
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    This study introduces an adaptive-matched filter (AMF) optimized by a genetic algorithm (GA) to effectively remove functional electrical stimulation (FES) artifacts from surface electromyography (EMG) signals, improving volitional EMG extraction.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Rehabilitation Technology

    Background:

    • Surface electromyography (EMG) is crucial for monitoring muscle activity.
    • Functional electrical stimulation (FES) often introduces artifacts that contaminate EMG signals.
    • Accurate EMG analysis is essential for effective FES-based rehabilitation.

    Purpose of the Study:

    • To develop a novel method for extracting volitional EMG signals corrupted by FES artifact.
    • To design and optimize an adaptive-matched filter (AMF) using a genetic algorithm (GA).

    Main Methods:

    • An adaptive-matched filter (AMF) was designed and optimized using a genetic algorithm (GA).
    • Simulated and real EMG data from seven subjects were processed using the GA-AMF and a comb filter.
    • EMG contamination was simulated at various FES artifact intensities.

    Main Results:

    • The GA-AMF filter demonstrated significantly higher correlation coefficients, signal-to-noise ratios, and lower normalized root mean square error in simulated tests compared to the comb filter.
    • Real EMG data filtered with GA-AMF showed greater power reduction than with the comb filter.
    • The GA-AMF effectively removed FES artifacts from both stimulated and adjacent muscles.

    Conclusions:

    • The GA-AMF filter successfully extracts volitional EMG from muscles affected by FES.
    • This method offers technical support for enhancing EMG feedback control in FES rehabilitation systems.