A Reliable Muscle Synergy Extraction Method based on Multivariate Curve Resolution-Alternating Least Squares

Abstract

Muscle synergy is an important approach to evaluate motor function for patients with neurological diseases. Nonnegative matrix factorization (NMF) is the most widely used muscle synergy extraction method from electromyography (EMG) data. However, NMF usually falls into local optimum and is susceptible to noise, which significantly limit the promotion of muscle synergy. In this paper, a reliable synergy extraction method based on multivariate curve resolution-alternating least squares (MCRALS) was put forward. Its performance was compared with NMF through analyzing the EMG data of upper limb motor. The repeatability and intra-subject consistency were used to evaluate the two methods. As a result, MCR-ALS provided unique resolution result and better repeatability and consistency in contrast to NMF. Thus, the results of this study are of significance for the expansion and application of muscle synergy in medicine.