PMID- 35720729
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 1662-4548 (Print)
IS  - 1662-453X (Electronic)
IS  - 1662-453X (Linking)
VI  - 16
DP  - 2022
TI  - Evaluation of Methods for the Extraction of Spatial Muscle Synergies.
PG  - 732156
LID - 10.3389/fnins.2022.732156 [doi]
LID - 732156
AB  - 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 (AEs), an unsupervised neural network, were recently 
      introduced to study bioinspired control and movement classification. In this 
      study, we evaluated the performance of five methods for the extraction of spatial 
      muscle synergy, namely, principal component analysis (PCA), independent component 
      analysis (ICA), factor analysis (FA), nonnegative matrix factorization (NMF), and 
      AEs 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. The signal-to-noise ratio (SNR) and 
      the number of channels (NoC) 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 with standard classification 
      methods, including K-nearest neighbors (KNN), linear discriminant analysis (LDA), 
      support vector machines (SVM), and Random Forest (RF). The results showed that 
      both SNR and NoC affected the outputs of the muscle synergy analysis. Although 
      AEs 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 was higher. Overall, the results 
      suggest selecting suitable methods when performing muscle synergy-related 
      analysis.
CI  - Copyright (c) 2022 Zhao, Wen, Zhang, Atzori, Muller, Xie and Scano.
FAU - Zhao, Kunkun
AU  - Zhao K
AD  - School of Mechanical Engineering, Southeast University, Nanjing, China.
FAU - Wen, Haiying
AU  - Wen H
AD  - School of Mechanical Engineering, Southeast University, Nanjing, China.
AD  - Engineering Research Center of New Light Sources Technology and Equipment, 
      Ministry of Education, Nanjing, China.
FAU - Zhang, Zhisheng
AU  - Zhang Z
AD  - School of Mechanical Engineering, Southeast University, Nanjing, China.
FAU - Atzori, Manfredo
AU  - Atzori M
AD  - Information Systems Institute, University of Applied Sciences Western Switzerland 
      (HES-SO Valais), Sierre, Switzerland.
AD  - Department of Neuroscience, University of Padova, Padua, Italy.
FAU - Muller, Henning
AU  - Muller H
AD  - Information Systems Institute, University of Applied Sciences Western Switzerland 
      (HES-SO Valais), Sierre, Switzerland.
AD  - Medical Faculty, University of Geneva, Geneva, Switzerland.
FAU - Xie, Zhongqu
AU  - Xie Z
AD  - School of Mechanical Engineering, Southeast University, Nanjing, China.
FAU - Scano, Alessandro
AU  - Scano A
AD  - UOS STIIMA Lecco - Human-Centered, Smart and Safe, Living Environment, Italian 
      National Research Council (CNR), Lecco, Italy.
LA  - eng
PT  - Journal Article
DEP - 20220602
PL  - Switzerland
TA  - Front Neurosci
JT  - Frontiers in neuroscience
JID - 101478481
PMC - PMC9202610
OTO - NOTNLM
OT  - autoencoder (AE)
OT  - factor analysis (FA)
OT  - independent component analysis (ICA)
OT  - muscle synergy
OT  - non-negative matrix factorization (NMF)
OT  - principal component analysis (PCA)
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/06/21 06:00
MHDA- 2022/06/21 06:01
PMCR- 2022/01/01
CRDT- 2022/06/20 03:45
PHST- 2021/06/28 00:00 [received]
PHST- 2022/05/04 00:00 [accepted]
PHST- 2022/06/20 03:45 [entrez]
PHST- 2022/06/21 06:00 [pubmed]
PHST- 2022/06/21 06:01 [medline]
PHST- 2022/01/01 00:00 [pmc-release]
AID - 10.3389/fnins.2022.732156 [doi]
PST - epublish
SO  - Front Neurosci. 2022 Jun 2;16:732156. doi: 10.3389/fnins.2022.732156. eCollection 
      2022.