将生理模型融入肌电分解，提升神经信号解析精度与效率
A Biophysical-Model-Informed Source Separation Framework For EMG Decomposition
- 结合解剖结构建模与生成方法，实现无监督反演
- 仿真验证显示估计更准确且计算成本显著降低
- 适合临床诊断、假肢控制等需个性化神经评估场景
神经接口技术的进展推动了人机交互、康复和神经肌肉诊断的发展。从表面肌电（sEMG）中分解运动单位（MU）是提取神经驱动信息的关键技术，但传统盲源分离（BSS）方法未考虑生物物理约束，限制了其准确性与可解释性。本文提出一种新型生物物理模型引导的源分离框架（BMISS），将基于MRI重建的解剖学精确前向肌电模型融入分解过程。通过生成建模，该方法可直接反演生物物理上合理的前向模型，以无监督方式估计神经驱动及运动神经元特性。在受控仿真环境中的实证验证表明，BMISS在保持更高运动单位估计保真度的同时，显著降低了计算开销。该框架为非侵入式、个性化的神经肌肉评估开辟新路径，适用于临床诊断、假肢控制与神经康复。
原文摘要 · Abstract (English)
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.
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