A Biophysical-Model-Informed Source Separation Framework For EMG Decomposition

TL;DR

This paper introduces BMISS, a novel framework that incorporates biophysical models into EMG source separation, improving motor unit decomposition accuracy and efficiency for neuromuscular analysis.

Contribution

The work presents a biophysical-model-informed approach that integrates MRI-based anatomical data into EMG decomposition, enabling unsupervised estimation of neural drive and motor neuron properties.

Findings

Higher fidelity motor unit estimation in simulations

Significantly reduced computational cost

Potential for personalized neuromuscular assessments

Abstract

Recent advances in neural interfacing have enabled significant improvements in human-computer interaction, rehabilitation, and neuromuscular diagnostics. Motor unit (MU) decomposition from surface electromyography (sEMG) is a key technique for extracting neural drive information, but traditional blind source separation (BSS) methods fail to incorporate biophysical constraints, limiting their accuracy and interpretability. In this work, we introduce a novel Biophysical-Model-Informed Source Separation (BMISS) framework, which integrates anatomically accurate forward EMG models into the decomposition process. By leveraging MRI-based anatomical reconstructions and generative modeling, our approach enables direct inversion of a biophysically accurate forward model to estimate both neural drive and motor neuron properties in an unsupervised manner. Empirical validation in a controlled simulated setting demonstrates that BMISS achieves higher fidelity motor unit estimation while significantly reducing computational cost compared to traditional methods. This framework paves the way for non-invasive, personalized neuromuscular assessments, with potential applications in clinical diagnostics, prosthetic control, and neurorehabilitation.