Compared with the above methods, the NMF method has its advantages in extracting the synergetic module by decomposing the EMG matrix into several low-dimensional spaces and time-dependent variables, which is widely applied in muscle synergy ( Amundsen Huffmaster et al., 2018 Bahadur et al., 2019 Matsuura et al., 2020). Later, some studies tried to use the SOBI method for muscle synergy estimation, but it is the best algorithm with four channels (no dimension reduction) and is not suitable for this study ( Belouchrani et al., 1997 Ebied et al., 2018). Here, with respect to both ICA and PCA, two types of blind source separation can reveal several patterns of muscle synergy but have limitations in the specific assumptions in the extracted muscle synergy (orthogonality for PCA and statistical independence for ICA) and the quite highly mean communality of the data ( Ivanenko et al., 2004 Weiss and Flanders, 2004 Esmaeili and Maleki, 2019). The present studies mainly depended on the theories of dimension reduction and blind source separation, such as independent component analysis (ICA), principle component analysis (PCA), second-order blind identification (SOBI), and non-negative matrix factorization (NMF). Over the last few years, extensive studies have sought methodologies to elucidate muscle synergy. However, it is still unclear how to choose the muscle activation pattern, and organize and coordinate muscles to mobilize different behaviors and movements. Additionally, some studies pointed out that the CNS is endowed with a set of intrinsically representative synergetic modules, and it can dominate some of the modules as a combination to involve different movements ( Israely et al., 2018 Gueugnon et al., 2019 Cheung et al., 2020 Jonsdottir et al., 2020). It is a common assumption that the central nervous system (CNS) with a modular structure can simplify motor tasks to low-dimensional modules by linear combinations of muscle synergy ( Cheung et al., 2012 Bizzi and Cheung, 2013 d’Avella and Lacquaniti, 2013), which refers to several muscles participating in a movement in a fixed combination ( Flash and Bizzi, 2016 Liang et al., 2021). Human movement is a highly complex activity produced by neuromuscular activation and biomechanical output ( Gottlieb, 1998). Our findings on muscle synergy will be of great significance to motor control and even to clinical assessment techniques. This study confirmed the theory of modular structure in the central nervous system, which yields a stable synergetic pattern under the same movement. Additionally, we also found shared synergy and special synergy in activation patterns among different movements. Furthermore, Spearman’s correlation analysis indicated significant similarities among HO-WE, HO-SU, and WE-SU ( p < 0.001). The results showed a highly modular similarity of the muscle synergy among subjects under the same movement. For this, we enrolled 10 healthy subjects to record the electromyography signal for NMF calculation. In this study, we introduced the non-negative matrix factorization (NMF) method to explore the muscle activation patterns and synergy structure under 6 types of movements, involving the hand open (HO), hand close (HC), wrist flexion (WF), wrist extension (WE), supination (SU), and pronation (PR). However, a few studies focus on the synergetic similarity and dissimilarity among different types of movements, especially for the upper extremity movements. Extensive studies have verified that it is the foundation to induce a complex movement by the modular combinations of several muscles with a synergetic relationship. A core issue in motor control is how the central nervous system generates and selects the muscle activation patterns necessary to achieve a variety of behaviors and movements.
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