Evaluation of matrix factorisation approaches for muscle synergy extraction

TL;DR

This study evaluates various matrix factorisation techniques for muscle synergy extraction, highlighting NMF as the most effective method under certain conditions and discussing factors influencing synergy estimation quality.

Contribution

It provides a comprehensive comparison of PCA, ICA, NMF, and SOBI for muscle synergy extraction using real and synthetic data, identifying optimal methods based on data characteristics.

Findings

NMF performs best with more channels than synergies.

Sparse synergy models and more channels improve estimation accuracy.

SOBI is a viable alternative with limited electrodes.

Abstract

The muscle synergy concept provides a widely-accepted paradigm to break down the complexity of motor control. In order to identify the synergies, different matrix factorisation techniques have been used in a repertoire of fields such as prosthesis control and biomechanical and clinical studies. However, the relevance of these matrix factorisation techniques is still open for discussion since there is no ground truth for the underlying synergies. Here, we evaluate factorisation techniques and investigate the factors that affect the quality of estimated synergies. We compared commonly used matrix factorisation methods: Principal component analysis (PCA), Independent component analysis (ICA), Non-negative matrix factorization (NMF) and second-order blind identification (SOBI). Publicly available real data were used to assess the synergies extracted by each factorisation method in the classification of wrist movements. Synthetic datasets were utilised to explore the effect of muscle synergy sparsity, level of noise and number of channels on the extracted synergies. Results suggest that the sparse synergy model and a higher number of channels would result in better-estimated synergies. Without dimensionality reduction, SOBI showed better results than other factorisation methods. This suggests that SOBI would be an alternative when a limited number of electrodes is available but its performance was still poor in that case. Otherwise, NMF had the best performance when the number of channels was higher than the number of synergies. Therefore, NMF would be the best method for muscle synergy extraction.