![]() ![]() In all cases, the original synergies were accurately reconstructed. (B) The five synergies recovered when the NMF algorithm was applied to the simulated EMG data. (A) Set of five simulated synchronous synergies used as the “ground-truth” for testing our method. ![]() Robustness of our method applied to synchronous synergies when varying the number of “ground-truth” synergies and the sources and levels of noise. Overall, these findings stress the effectiveness of the decoding metric in systematically assessing muscle synergy decompositions in task space.Īrm movement muscle synergies reaching single-trial analysis task decoding. We find that time-varying and synchronous synergies with similar number of parameters are equally efficient in task decoding, suggesting that in this experimental paradigm they are equally valid representations of muscle synergies. We then show that it can be applied to different types of muscle synergy decomposition and illustrate its applicability to real data by using it for the analysis of EMG recordings during an arm pointing task. In this paper, we first validate the method on plausibly simulated EMG datasets. The task decoding based metric evaluates quantitatively the mapping between synergy recruitment and task identification and automatically determines the minimal number of synergies that captures all the task-discriminating variability in the synergy activations. The procedure is based on single-trial task decoding from muscle synergy activation features. Unlike previous methods considering the total variance of muscle patterns (VAF based metrics), our approach focuses on variance discriminating execution of different tasks. To address this question, here we conceive and develop a novel computational framework to evaluate muscle synergy decompositions in task space. Yet, little is known about the extent to which the combination of those synergies encodes task-discriminating variations of muscle activity in individual trials. ![]() Typically, the quality of synergy decompositions is assessed by computing the Variance Accounted For (VAF). Several efficient dimensionality reduction algorithms that extract putative synergies from electromyographic (EMG) signals have been developed. Muscle synergies, i.e., invariant coordinated activations of groups of muscles, have been proposed as building blocks that the central nervous system (CNS) uses to construct the patterns of muscle activity utilized for executing movements. ![]()
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