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The muscle synergy concept provides a widely-accepted paradigm to break downthe complexity of motor control. In order to identify the synergies, differentmatrix factorisation techniques have been used in a repertoire of fields suchas prosthesis control and biomechanical and clinical studies. However, therelevance of these matrix factorisation techniques is still open for discussionsince there is no ground truth for the underlying synergies. Here, we evaluatefactorisation techniques and investigate the factors that affect the quality ofestimated 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 synergiesextracted by each factorisation method in the classification of wristmovements. Synthetic datasets were utilised to explore the effect of musclesynergy sparsity, level of noise and number of channels on the extractedsynergies. Results suggest that the sparse synergy model and a higher number ofchannels would result in better-estimated synergies. Without dimensionalityreduction, SOBI showed better results than other factorisation methods. Thissuggests that SOBI would be an alternative when a limited number of electrodesis available but its performance was still poor in that case. Otherwise, NMFhad the best performance when the number of channels was higher than the numberof synergies. Therefore, NMF would be the best method for muscle synergyextraction.

Evaluation of matrix factorisation approaches for muscle synergy extraction