Gao Zhi X, Wu Xiao Y, Xiong Qi L, Deng Chun F, Xiao Nong, Liu Yuan, Chen Yu X, Hou Wen S
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5966-5969. doi: 10.1109/EMBC.2018.8513568.
It has been widely accepted that the central nervous system (CNS) modulates muscle synergies to simplify motion control. However, it is still unclear that if there is a synergistic recruitment strategy to organize oscillation components of surface electromyography (sEMG) signals for limb movement. The sEMG signals were recorded from bilateral biceps brachii (BB) and triceps brachii (TB) muscles during infant crawling. The multivariate empirical mode decomposition (MEMD) was applied to decompose multi-channel sEMG signals into multi-scale oscillations. Then, non-negative matrix factorization (NMF) method was employed to extract oscillation synergy patterns. The results indicated that there were three stable oscillation synergies in sEMG signals for crawling movement, and the recruitment coefficient curves reflected the role of muscle during crawling movement. Our preliminary work suggested that synergistic recruitment of multi-scale oscillation components maybe a new way to understand the organization of MU recruitment strategy by the CNS.
中枢神经系统（CNS）调节肌肉协同作用以简化运动控制，这一点已被广泛接受。然而，目前仍不清楚是否存在一种协同募集策略来组织表面肌电图（sEMG）信号的振荡成分以实现肢体运动。在婴儿爬行过程中，从双侧肱二头肌（BB）和肱三头肌（TB）记录了sEMG信号。应用多变量经验模式分解（MEMD）将多通道sEMG信号分解为多尺度振荡。然后，采用非负矩阵分解（NMF）方法提取振荡协同模式。结果表明，在爬行运动的sEMG信号中存在三种稳定的振荡协同作用，且募集系数曲线反映了肌肉在爬行运动中的作用。我们的初步工作表明，多尺度振荡成分的协同募集可能是理解中枢神经系统运动单位募集策略组织方式的一种新途径。
