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Extracting synergies in gait: using EMG variability to evaluate control strategies
Author(s) -
Rajiv Ranganathan,
Chandramouli Krishnan
Publication year - 2012
Publication title -
journal of neurophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 245
eISSN - 1522-1598
pISSN - 0022-3077
DOI - 10.1152/jn.01112.2011
Subject(s) - electromyography , coupling (piping) , gait , treadmill , artificial neural network , physical medicine and rehabilitation , motor control , task (project management) , computer science , neural activity , pattern recognition (psychology) , psychology , artificial intelligence , neuroscience , medicine , physical therapy , engineering , mechanical engineering , systems engineering
There has been extensive debate as to whether muscle synergies in motor tasks reflect underlying neural organization or simply correlations in muscle activity that are imposed by the task. One possible means of distinguishing these two alternatives is through the analysis of variability in the electromyogram (EMG). Here, we simulated EMG in eight lower-limb muscles and introduced hypothetical neural coupling between specific muscle groups. Neural coupling was simulated by introducing correlations in the neural activation commands to different muscles (positive, negative, or zero coupling). When the entire EMG signal was used for analysis, the extracted synergies reflected only simultaneous muscle activity, regardless of the neural coupling between the muscles. On the other hand, examining the variability in the EMG after subtracting the ensemble average was successful in identifying the simulated neural coupling. The extracted synergies from these two methods were also different when we analyzed data from participants during treadmill walking. The results emphasize the importance of examining EMG variability to understand the neural basis of muscle synergies.

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