NDLI: Practical limits on muscle synergy identification by non-negative matrix factorization in systems with mechanical constraints

Content Provider	Springer Nature Link
Author	Burkholder, Thomas J. Antwerp, Keith W.
Copyright Year	2012
Abstract	Statistical decomposition, including non-negative matrix factorization (NMF), is a convenient tool for identifying patterns of structured variability within behavioral motor programs, but it is unclear how the resolved factors relate to actual neural structures. Factors can be extracted from a uniformly sampled, low-dimension command space. In practical application, the command space is limited, either to those activations that perform some task(s) successfully or to activations induced in response to specific perturbations. NMF was applied to muscle activation patterns synthesized from low dimensional, synergy-like control modules mimicking simple task performance or feedback activation from proprioceptive signals. In the task-constrained paradigm, the accuracy of control module recovery was highly dependent on the sampled volume of control space, such that sampling even 50 % of control space produced a substantial degradation in factor accuracy. In the feedback paradigm, NMF was not capable of extracting more than four control modules, even in a mechanical model with seven internal degrees of freedom. Reduced access to the low-dimensional control space imposed by physical constraints may result in substantial distortion of an existing low dimensional controller, such that neither the dimensionality nor the composition of the recovered/extracted factors match the original controller.
Starting Page	187
Ending Page	196
Page Count	10
File Format	PDF
ISSN	01400118
Journal	Medical and Biological Engineering and Computing
Volume Number	51
Issue Number	1-2
e-ISSN	17410444
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-11-03
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neural control Muscle synergy Non-negative matrix factorization Mathematical methods Biomechanics Human Physiology Biomedical Engineering Imaging Radiology Computer Applications
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering Computer Science Applications

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