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    Understanding muscle synergies in motor control is key for rehabilitation. This study reveals how EMG signal processing, specifically filter cut-offs and normalization, impacts muscle synergy analysis and inter-subject similarity.

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    Area of Science:

    • Biomechanics
    • Neuroscience
    • Rehabilitation Engineering

    Background:

    • Muscle synergies are fundamental to understanding motor control and are increasingly used in rehabilitation strategies.
    • Variability in data pre-processing and analysis methods hinders the comparison of muscle synergy results across studies.

    Purpose of the Study:

    • To investigate the impact of different electromyography (EMG) pre-processing techniques on muscle synergy extraction.
    • To determine how variations in filter cut-off frequencies and normalization methods affect synergy weights and inter-subject similarity (ISS).

    Main Methods:

    • Experimental data from a 15-muscle upper-limb reaching task were analyzed.
    • Canonical EMG processing was applied, varying band-pass filter cut-offs ([20-500 Hz] vs. [50-500 Hz]) and normalization methods (maximum voluntary contraction - MVC - vs. maximum signal amplitude - SELF).
    • Low-pass (LP) filters with cut-offs of 0.5 Hz, 4 Hz, 10 Hz, and 20 Hz were compared.

    Main Results:

    • Synergy weights were not significantly affected by normalization or band-pass filtering.
    • Normalization methods altered the variance explained by synergies, a common criterion for model order selection.
    • Increasing LP filter cut-off decreased the variance accounted for by synergies and affected individual muscle contributions.
    • Extreme smoothing (LP cut-off 0.5 Hz) improved muscle contrast but unpredictably affected ISS.

    Conclusions:

    • EMG pre-processing choices significantly influence muscle synergy analysis, particularly regarding variance explained and muscle contributions.
    • Standardizing EMG processing is crucial for reliable and comparable muscle synergy research in motor control and rehabilitation.
    • Further research is needed to optimize EMG pre-processing for robust muscle synergy extraction and application.