Characterization of whole-body muscle activity during reaching movements using space-by-time modularity and functional similarity analysis

Voluntary movement is hypothesized to rely on a few low-dimensional structures, termed muscle synergies, whose recruitment translates task goals into effective muscle activity. However, the relationship of the synergies with the characteristics of the performed movements remains largely unexplored. To address this question, we recorded a comprehensive dataset of muscle activity during a variety of whole-body pointing movements. We decomposed the electromyographic (EMG) signals using a space-by-time modularity model which encompasses the main types of synergies. We then used a task decoding and information theoretic analysis to probe the role of each synergy by mapping it to specific task parameters. We found that the temporal and spatial aspects of the movements were encoded by different temporal and spatial muscle synergies, respectively, indicating that the identified synergies are tailored with complementary roles to account for the major movement attributes. This approach led to the development of a novel computational framework for comparing muscle synergies from different datasets according to their functional role. This functional similarity analysis yielded a small set of temporal and spatial synergies that describes the main features of whole-body reaching