表面肌电信号分解的新探索

Electromyography (EMG) is the temporal and spatial summation of motor unit action potential (MUAP) trains, originated from active spinal motor neurons. EMG decomposition can be considered as the reverse process of the EMG signal generation. The research on EMG decomposition is of great importance in terms of exploring the control mechanism of human neuromuscular system and a variety of clinical applications. . Taking advantage of the noninvasive character, surface EMG (SEMG) examination has much broader application prospect than the traditional intramuscular EMG (IEMG). However, due to the complex mechanism of SEMG, its decomposition is the most challenging task in the EMG field. This proposal is designed to conduct a novel exploration on SEMG decomposition targeting moderate muscle contraction force, based on the combination of advanced signal collection and processing techniques. For signal collection, the flexible high-density 2-dimensional electrode array will be used and optimally designed as the SEMG sensor to improve the discrimination of MUAP waveforms and reduce the number or extent of superposed MUAP waveforms. For signal processing, the temporal and spatial information associated with the MU activity will be discovered from the multi-channel SEMG recordings, using a series of advanced signal processing algorithms such as pattern recognition, blind source separation and matching filtering, with the attempt to study on some effective solutions to decomposition of superposed MUAP waveforms. Based on the above-mentioned research, the SEMG simulation approach and "two sources" method will be applied respectively to evaluate the performance of the proposed SEMG decomposition methods. By further optimizing the experimental schemes, it is designed to bring a systematic and promising solution to SEMG decomposition at the condition of moderate muscle contraction force.

肌电信号是由运动单位动作电位（MUAP）序列在检测电极处的时空叠加形成的信号。肌电分解是其形成的逆过程，对于神经肌肉控制机理研究和临床诊断具有重要的研究意义和应用价值。表面肌电（SEMG）因其检测无创性的特点，相比插入式肌电应用场合更广，但其由于产生机制复杂，对其分解是肌电研究领域的难题。本项目拟结合先进的信号检测技术与处理方法，对在中度收缩力下的SEMG分解进行新的探索。在信号检测方面，将采用并优化设计柔性高密度电极阵列传感器，增强信号中MUAP波形区分度，减少叠加波形数目或叠加程度；在信号处理方面，将基于模式识别、盲源分离等先进信号处理技术，深入挖掘多通道SEMG包含的时空信息，研究解决叠加MUAP波形分解这一经典难题。在上述研究基础上，分别采用信号仿真和"双源"法验证分解方案的可靠性，通过实验方案的不断优化，提出在中度收缩力条件下可用于分解SEMG信号完善的解决方案。

肌电信号(electromyography, EMG)是由运动单位动作电位(Motor Unit action potential, MUAP)序列在检测电极处的时空叠加形成的信号。肌电分解是其形成的逆过程，对于神经肌肉控制机理研究和临床诊断具有重要的研究意义和应用价值。表面肌电(surface EMG, sEMG)因其检测无创性的特点，相比插入式EMG应用场景更广，但其由于产生机制复杂，对其分解是EMG研究领域的难题。本项目对sEMG信号分解中的一些关键问题进行了研究，采用了先进的信号检测技术与处理方法，对在中度收缩力下的sEMG分解进行性的探索。在信号检测方面，采用了柔性高密度电极阵列传感器，增强信号中MUAP波形区分度，减少叠加波形或叠加程度；在信号处理方面，将结合基于模式识别、盲源分离等先进信号处理技术，开发了约束快速独立分量分析算法，深入挖掘多通道sEMG包含的时空信息，研究解决了叠加MUAP波形分解这一经典难题。在上述研究基础上，分别采用信号仿真和“双源”法验证分解方案的可靠性，通过实验方案的不断优化，实现了在中度收缩力条件下可用于分解sEMG信号完善的解决方案。