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Task-discriminative space-by-time factorization of muscle activity

Overview of attention for article published in Frontiers in Human Neuroscience, July 2015
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Title
Task-discriminative space-by-time factorization of muscle activity
Published in
Frontiers in Human Neuroscience, July 2015
DOI 10.3389/fnhum.2015.00399
Pubmed ID
Authors

Ioannis Delis, Stefano Panzeri, Thierry Pozzo, Bastien Berret

Abstract

Movement generation has been hypothesized to rely on a modular organization of muscle activity. Crucial to this hypothesis is the ability to perform reliably a variety of motor tasks by recruiting a limited set of modules and combining them in a task-dependent manner. Thus far, existing algorithms that extract putative modules of muscle activations, such as Non-negative Matrix Factorization (NMF), identify modular decompositions that maximize the reconstruction of the recorded EMG data. Typically, the functional role of the decompositions, i.e., task accomplishment, is only assessed a posteriori. However, as motor actions are defined in task space, we suggest that motor modules should be computed in task space too. In this study, we propose a new module extraction algorithm, named DsNM3F, that uses task information during the module identification process. DsNM3F extends our previous space-by-time decomposition method (the so-called sNM3F algorithm, which could assess task performance only after having computed modules) to identify modules gauging between two complementary objectives: reconstruction of the original data and reliable discrimination of the performed tasks. We show that DsNM3F recovers the task dependence of module activations more accurately than sNM3F. We also apply it to electromyographic signals recorded during performance of a variety of arm pointing tasks and identify spatial and temporal modules of muscle activity that are highly consistent with previous studies. DsNM3F achieves perfect task categorization without significant loss in data approximation when task information is available and generalizes as well as sNM3F when applied to new data. These findings suggest that the space-by-time decomposition of muscle activity finds robust task-discriminating modular representations of muscle activity and that the insertion of task discrimination objectives is useful for describing the task modulation of module recruitment.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 26%
Researcher 8 23%
Student > Master 4 11%
Other 2 6%
Professor > Associate Professor 2 6%
Other 5 14%
Unknown 5 14%
Readers by discipline Count As %
Engineering 11 31%
Neuroscience 10 29%
Medicine and Dentistry 3 9%
Sports and Recreations 2 6%
Nursing and Health Professions 2 6%
Other 1 3%
Unknown 6 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 July 2015.
All research outputs
#17,765,638
of 22,816,807 outputs
Outputs from Frontiers in Human Neuroscience
#5,707
of 7,148 outputs
Outputs of similar age
#176,877
of 262,950 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#124
of 158 outputs
Altmetric has tracked 22,816,807 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,148 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 262,950 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 158 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.