Chen Chen, Li Dongxuan, Xia Miaojuan
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
J Neuroeng Rehabil. 2025 Mar 14;22(1):60. doi: 10.1186/s12984-025-01595-y.
Surface electromyography (EMG) decomposition is crucial for identifying motor neuron activities by analyzing muscle-generated electrical signals. This study aims to develop and validate a novel motor unit action potential (MUAP)-based method for surface EMG decomposition, addressing the limitations of traditional blind source separation (BSS)-based techniques in computation complexity and motor unit (MU) tracking.
Within the framework of the convolution kernel compensation algorithm, we developed a MUAP-based decomposition algorithm by reconstructing the MU filters from MUAPs and evaluated its performance using both simulated and experimental datasets. A systematic analysis was conducted on various factors affecting decomposition performance, including MU filter reconstruction methods, EMG covariance matrices, MUAP extraction techniques, and extending factors. The proposed method was subsequently compared to representative BSS-based techniques, such as convolution kernel compensation.
The MUAP-based method significantly outperformed traditional BSS-based techniques in identifying more MUs and achieving better accuracy, particularly under noisy conditions. It demonstrated superior performance with increased signal complexity and effectively tracked motor units consistently across decompositions. In addition, directly applying the MU filters reconstructed from MUAPs to decomposition exhibited marked computational efficiency.
The MUAP-based method enhances EMG decomposition accuracy, robustness, and efficiency, offering reliable motor unit tracking and real-time processing capabilities. These advancements highlight its potential for clinical diagnostics and neurorehabilitation, representing a promising step forward in non-invasive motor neuron analysis.
表面肌电图（EMG）分解对于通过分析肌肉产生的电信号来识别运动神经元活动至关重要。本研究旨在开发并验证一种基于运动单位动作电位（MUAP）的新型表面EMG分解方法，以解决传统基于盲源分离（BSS）技术在计算复杂度和运动单位（MU）跟踪方面的局限性。
在卷积核补偿算法框架内，我们通过从MUAP重建MU滤波器开发了一种基于MUAP的分解算法，并使用模拟和实验数据集评估其性能。对影响分解性能的各种因素进行了系统分析，包括MU滤波器重建方法、EMG协方差矩阵、MUAP提取技术和扩展因素。随后将所提出的方法与代表性的基于BSS的技术（如卷积核补偿）进行比较。
基于MUAP的方法在识别更多运动单位和实现更高准确性方面明显优于传统基于BSS的技术，尤其是在噪声条件下。它在信号复杂度增加时表现出卓越性能，并在整个分解过程中有效地持续跟踪运动单位。此外，将从MUAP重建的MU滤波器直接应用于分解显示出显著的计算效率。
基于MUAP的方法提高了EMG分解的准确性、鲁棒性和效率，提供了可靠的运动单位跟踪和实时处理能力。这些进展突出了其在临床诊断和神经康复中的潜力，代表了无创运动神经元分析向前迈出的有希望的一步。
