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    Summary
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    This study introduces a novel deep learning framework to interpret microscopic motor unit (MU) activity from surface electromyogram (sEMG) signals for precise muscle force estimation, achieving high accuracy.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Interpreting microscopic motor unit (MU) activity from surface electromyogram (sEMG) signals for neural control decoding is challenging.
    • Accurate muscle force estimation relies on understanding complex neural drive information.

    Purpose of the Study:

    • To propose and validate a novel framework using hybrid encoder-decoder deep networks for precise muscle force estimation.
    • To process microscopic neural drive information derived from high-density sEMG (HD-sEMG) decomposition.

    Main Methods:

    • HD-sEMG decomposition using the progressive FastICA peel-off algorithm.
    • Muscle twitch force modeling to convert action potentials into twitch forces.
    • Hybrid encoder-decoder deep networks to analyze spatial and temporal MU force contributions.

    Main Results:

    • The framework achieved a mean root mean square error of 6.62% ± 1.26% for muscle force estimation.
    • A mean coefficient of determination of 0.95 ± 0.03 was obtained, indicating high accuracy.
    • The proposed method significantly outperformed three common alternative methods (p < 0.001).

    Conclusions:

    • The developed framework offers a robust solution for interpreting microscopic neural drive.
    • This approach successfully predicts muscle force with high precision using HD-sEMG data.
    • The study advances the field of neural decoding and muscle force estimation.