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Abstract Objective. Neural signals in residual muscles of amputated limbs are frequently decoded to control powered prostheses. Yet myoelectric controllers assume muscle activity of residual muscle is similar to that of intact muscle. This study sought to understand potential changes to motor unit (MU) properties after limb amputation. Approach. Six people with unilateral transtibial amputation were recruited. Surface electromyogram (EMG) of residual and intact tibialis anterior (TA) and gastrocnemius (GA) muscles were recorded while subjects traced profiles targeting up to 20 and 35% of maximum activation for each muscle (isometric for intact limbs). EMG was decomposed into groups of motor unit (MU) spike trains. MU recruitment thresholds, action potential amplitudes (MU size), and firing rates were correlated to model Henneman’s size principle, the onion-skin phenomenon, and rate-size associations. Organization (correlation) and modulation (rates of change) of relations were compared between intact and residual muscles. Main results. The residual TA exhibited significantly lower correlation and flatter slopes in the size principle and onion-skin, and each outcome covaried between the MU relations. The residual GA was unaffected for most subjects. Subjects trained prior with myoelectric prostheses had minimally affected slopes in the TA. Rate-size association correlations were preserved, but both residual muscles exhibited flatter decay rates. Significance. We showed peripheral neuromuscular damage also leads to spinal-level functional reorganization. Our findings suggest models of MU recruitment and discharge patterns for residual muscle EMG generation need reparameterization to account for disturbances observed. In the future, tracking MU pool adaptations may also provide a biomarker of neuromuscular control to aid training with myoelectric prostheses.
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Cross-person decomposition of surface electromyogram for efficient motor unit activity predictions
Abstract Objective. Accurate prediction of motor unit (MU) discharge activity from surface electromyogram (sEMG) signals is critical for understanding neuromuscular control and for enabling practical neural interface applications. However, current MU decomposition approaches rely on person-specific data, limiting their generalizability. Approach. We developed a cross-person decomposition framework and validated the algorithm using synthesized high-density sEMG data by convoluting simulated MU firing spike trains with action potential templates derived from human experimental data. We first obtained separation matrix from multiple training subjects and applied them to decompose sEMG signals from unseen test subjects. This allowed us to obtain MU spike trains. The predicted outcomes were then compared with the ground truth across multiple metrics, including spike detection accuracy, MU firing rate (FR), waveform similarity of motor unit action potentials (MUAP), and MU recruitment thresholds. Main results. Our results demonstrated strong agreement between predicted and true MU activity. Specifically, we found high R² values (≥0.95) for the populational FR, and the coefficient of variation of FR remained stable across different MU retention thresholds. The MU similarity analyses revealed that the predicted MUAPs closely matched ground truth counterparts both in waveform shape and spatial distribution. Furthermore, recruitment thresholds exhibited strong linear relation (R² = 0.98 ± 0.006) with minimal error. Significance. These findings demonstrate the feasibility of efficient cross-person MU decomposition with minimal accuracy loss, laying the groundwork for generalized, plug-and-play myoelectric systems in neurophysiology, neuroprosthetic, and rehabilitation applications.
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- Award ID(s):
- 2246162
- PAR ID:
- 10628806
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Journal of Neural Engineering
- ISSN:
- 1741-2560
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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