Title: Evaluation of matrix factorisation approaches for muscle synergy extraction,
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.