Cross-person decomposition of surface electromyogram for efficient motor unit activity predictions

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 MU action potentials (MUAPs), and MU recruitment thresholds. Main results. Our results demonstrated strong agreement between predicted and true MU activity. Specifically, we found high R 2 values (⩾0.95) for the populational FR, and the coefficient of variation of FR remained stable across different MU retention thresholds. The MU similarity analyzes revealed that the predicted MUAPs closely matched ground truth counterparts both in waveform shape and spatial distribution. Furthermore, recruitment thresholds exhibited strong linear relation ( R 2 = 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.