Human motion control system has a high degree of nonlinear characteristics. Through quantitative evaluation of the nonlinear coupling strength between surface electromyogram (sEMG) signals, we can get the functional state of the muscles related to the movement, and then explore the mechanism of human motion control. In this paper, wavelet packet decomposition and n: m coherence analysis are combined to construct an intermuscular cross-frequency coupling analysis model based on wavelet packet- n: m coherence. In the elbow flexion and extension state with 30% maximum voluntary contraction force (MVC), sEMG signals of 20 healthy adults were collected. Firstly, the subband components were obtained based on wavelet packet decomposition, and then the n: m coherence of subband signals was calculated to analyze the coupling characteristics between muscles. The results show that the linear coupling strength (frequency ratio 1:1) of the cooperative and antagonistic pairs is higher than that of the nonlinear coupling (frequency ratio 1:2, 2:1 and 1:3, 3:1) under the elbow flexion motion of 30% MVC; the coupling strength decreases with the increase of frequency ratio for the intermuscular nonlinear coupling, and there is no significant difference between the frequency ratio n: m and m: n. The intermuscular coupling in beta and gamma bands is mainly reflected in the linear coupling (1:1), nonlinear coupling of low frequency ratio (1:2, 2:1) between synergetic pair and the linear coupling between antagonistic pairs. The results show that the wavelet packet- n: m coherence method can qualitatively describe the nonlinear coupling strength between muscles, which provides a theoretical reference for further revealing the mechanism of human motion control and the rehabilitation evaluation of patients with motor dysfunction.
人体运动控制系统具有高度的非线性特性，通过量化评价表面肌电（sEMG）信号间的非线性耦合强度，可以得到运动相关肌肉的功能状态，进而探究人体运动控制的机制。本文将小波包分解和 n∶ m 相干性分析相结合，构建基于小波包— n∶ m 相干性的肌间交叉频率耦合分析模型，探究肌电信号间的非线性耦合关系。在维持 30% 最大自主收缩力（MVC）的肘部屈伸状态下，采集 20 名健康成年人的 sEMG 信号，首先基于小波包分解获取子带分量，然后将子带信号进行 n∶ m 相干性计算，分析肌间耦合特征。结果表明：30%MVC 的肘部屈曲运动下，协同肌对和拮抗肌对的线性耦合（频率比为 1∶1 时）强度高于非线性耦合（频率比为 1∶2、2∶1 和 1∶3、3∶1 时）；对于肌间非线性耦合，随着频率比的增大，耦合强度随之降低，且频率比为 n∶ m 和 m∶ n 之间没有明显的耦合强度差异；beta 和 gamma 频段内的肌间耦合主要体现在协同肌对之间的线性耦合（1∶1）和低频率比的非线性耦合（1∶2、2∶1）以及拮抗肌对之间的线性耦合上。以上说明：小波包— n∶ m 相干性方法可以定性、定量地描述肌间非线性耦合强度，为深入揭示人体运动控制机制和运动功能障碍患者的康复评价提供理论参考。.
Keywords: electromyography signal; n:m coherence analysis; nonlinear coupling; wavelet packet decomposition.