基于SEONS算法的表面肌电信号分解方法研究

目的 探讨表面肌电信号(sEMG)的分解问题,以检测神经肌肉系统的运动单位发放的信息.方法 采用二阶非平稳源分离(SEONS)算法以及FastICA方法对肌肉轻度收缩情况(10% MVC)的sEMG进行分解研究,并对分离信号中的运动单位动作电位波形进行检测判断. 结果两种方法均能较有效地提取隐含在sEMG信号中的运动单位发放信息,但由于sEMG信号是一种复杂的非平稳信号,两种方法的分离结果存在一定的差异性. 结论 SEONS算法从非平稳性的角度来考虑信号的盲源分离问题,较符合sEMG信号的非平稳特征,因而可应用于sEMG信号的分解研究.

A Study on the Decomposition of Surface EMG Signals Based on Second Order Non-stationary Source Separation

Objective To investigate the decomposition method of surface EMG(sEMG)signals based on Blind Source Separation and to detect the the motor unit action potential(MUAP)information.Methods Utilizing the sEMG signals recorded at low muscle contraction force(10% MVC),the methods of second order non-stationary source separation(SEONS)and FastICA were explored to analyze the sEMG signals decomposition.Results The experiment results showed that the MUAP information could be acquired by spike detection and pattern recognition after the decomposition of recorded sEMG signals using the proposed algorithm and FastICA method,but a little difference occurred due to the complexity of sEMG signals.Conclusion The non-stationary characteristic of sEMG signals is considered by the SEONS algorithm,and the proposed method can be applied in the sEMG signals decomposition.