AUTHOR=Zhao Kunkun , Wen Haiying , Zhang Zhisheng , Atzori Manfredo , Müller Henning , Xie Zhongqu , Scano Alessandro 

TITLE=Evaluation of Methods for the Extraction of Spatial Muscle Synergies

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.732156

DOI=10.3389/fnins.2022.732156

ISSN=1662-453X

ABSTRACT=Muscle synergies have been largely used in many application fields, including motor control studies, prosthesis control, movement classification, rehabilitation, and clinical studies. Due to the complexity of the motor control system, the full repertoire of the underlying synergies has been identified only for some classes of movements and scenarios. Several extraction methods have been used to extract muscle synergies. However, some of these methods may not effectively capture the nonlinear relationship between muscles and impose constraints on input signals or extracted synergies. Moreover, other approaches such as autoencoders, an unsupervised neural network, were recently introduced to study bio-inspired control and movement classification. In this study, we evaluated the performance of five methods for spatial muscle synergy extraction: principal component analysis (PCA), independent component analysis (ICA), factor analysis (FA), non-negative matrix factorization (NMF), and autoencoders (AE) using simulated data and a publicly available database. To analyze the performance of the considered extraction methods with respect to several factors, we generated a comprehensive set of simulated data (ground truth), including spatial synergies and temporal coefficients. Signal to noise ratio (SNR) and the number of channels (NoC) was varied when generating simulated data to evaluate their effects on ground truth reconstruction. This study also tested the efficacy of each synergy extraction method when coupled to standard classification methods, including K-nearest neighbors (KNN), linear discriminant analysis (LDA), support vector machines (SVM), and random forests (RF). The results showed that both SNR and NoC affected the outputs of muscle synergy analysis. Though autoencoders showed better performance than FA in variance accounted for and PCA in synergy vector similarity and activation coefficient similarity, NMF and ICA outperformed the other three methods. Classification tasks showed that classification algorithms were sensitive to synergy extraction methods, while KNN and RF outperformed the other two methods for all extraction methods; in general, the classification accuracy of NMF and PCA were higher. Overall, the results suggest selecting suitable methods when performing muscle synergy-related analysis.