![]() ![]() 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. 7Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK.6Center for Neuroscience and Cognitive Systems Istituto Italiano di Tecnologia, Rovereto, Italy.5INSERM, U1093, Action Cognition et Plasticité Sensorimotrice, Dijon, France.4Institut Universitaire de France, Université de Bourgogne, Campus Universitaire, UFR STAPS Dijon, France.3UR CIAMS, EA 4532 – Motor Control and Perception Team, Université Paris-Sud 11, Orsay, France.2Communication, Computer and System Sciences Department, Doctoral School on Life and Humanoid Technologies, University of Genoa, Genoa, Italy.1Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia, Genoa, Italy. ![]() Ioannis Delis 1,2* Bastien Berret 1,3 Thierry Pozzo 1,4,5 Stefano Panzeri 6,7* ![]()
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