{"ID":2863846,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17822","arxiv_id":"2510.17822","title":"A Biophysical-Model-Informed Source Separation Framework For EMG Decomposition","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.17822","url_pdf":"https://arxiv.org/pdf/2510.17822v1","authors":"[\"D. Halatsis\",\"P. Mamidanna\",\"J. Pereira\",\"D. Farina\"]","published":"2025-09-29T17:53:52Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"cs.AI\"]","methods":"[]","has_code":false}