Otherwise, NMF had the best performance when the number of channels was higher than the number of synergies. 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. Without dimensionality reduction, SOBI showed better results than other factorisation methods. Results suggest that the sparse synergy model and a higher number of channels would result in better estimated synergies. Synthetic datasets were utilised to explore the effect of muscle synergy sparsity, level of noise and number of channels on the extracted synergies. Publicly available real data were used to assess the synergies extracted by each factorisation method in the classification of wrist movements. 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). Here, we evaluate factorisation techniques and investigate the factors that affect the quality of estimated synergies. However, the relevance of these matrix factorisation techniques is still open for discussion since there is no ground truth for the underlying synergies. 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. The muscle synergy concept provides a widely-accepted paradigm to break down the complexity of motor control.
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