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. 2017 Oct 18;7:13465. doi: 10.1038/s41598-017-13766-6

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Separating the interferent electromyogram into its central and peripheral components. (A) High-density electromyograms (HD-EMGs) visualized for 13 out of 64 channels located in the middle column of a 13 × 5 electrode grid. This signal is recorded from the soleus muscle during a 30% maximal voluntary contraction performed with ankle joint at neutral position. (B) Experimental ankle plantar-dorsi flexion moment (continuous curve) depicted synchronously with soleus HD-EMG (A) and the decoded motor neuron discharge events (spike trains). (C) The interferent HD-EMG (A) is decomposed into motor unit (MU) action potentials (peripheral component) and motor neuron spike trains (central component, discrete spikes in B). Spike trains are combined together to form the cumulative spike train, i.e. an accurate estimate of the net neural drive produced by the nervous system in the control of a muscle. The decoded neural drive is subsequently used as part of the neural data-driven modeling formulation (Figs 3–5).