Investigating reduction of dimensionality during single-joint elbow movements: a case study on muscle synergies

Chiovetto, E., Berret, B., Delis, I., Panzeri, S. and Pozzo, T. (2013) Investigating reduction of dimensionality during single-joint elbow movements: a case study on muscle synergies. Frontiers in Computational Neuroscience, 7, Art.11. (doi: 10.3389/fncom.2013.00011)

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most well-known are those of synchronous, time-varying and temporal muscle synergies. Each one of them is based on a different mathematical model used to factor some EMG array recordings collected during the execution of variety of motor tasks into a well-determined spatial, temporal or spatio-temporal organization. This plurality of definitions and their separate application to complex tasks have so far complicated the comparison and interpretation of the results obtained across studies, and it has always remained unclear why and when one synergistic decomposition should be preferred to another one. By using well-understood motor tasks such as elbow flexions and extensions, we aimed in this study to clarify better what are the motor features characterized by each kind of decomposition and to assess whether, when and why one of them should be preferred to the others. We found that three temporal synergies, each one of them accounting for specific temporal phases of the movements could account for the majority of the data variation. Similar performances could be achieved by two synchronous synergies, encoding the agonist-antagonist nature of the two muscles considered, and by two time-varying muscle synergies, encoding each one a task-related feature of the elbow movements, specifically their direction. Our findings support the notion that each EMG decomposition provides a set of well-interpretable muscle synergies, identifying reduction of dimensionality in different aspects of the movements. Taken together, our findings suggest that all decompositions are not equivalent and may imply different neurophysiological substrates to be implemented.

Item Type:Articles
Additional Information:This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.
Glasgow Author(s) Enlighten ID:Panzeri, Professor Stefano and Delis, Dr Ioannis
Authors: Chiovetto, E., Berret, B., Delis, I., Panzeri, S., and Pozzo, T.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Frontiers in Computational Neuroscience
Publisher:Frontiers Research Foundation
Copyright Holders:Copyright © 2013 The Authors
First Published:First published in Frontiers in Computational Neuroscience 7:11
Publisher Policy:Reproduced under a Creative Commons License

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