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. 2018 Aug 23;8:12657. doi: 10.1038/s41598-018-31174-2

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© The Author(s) 2018

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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Muscle Synergies extracted at baseline. Four muscles synergies could well represent the whole dataset (a). The RMS values of the activation patterns of each synergy at each target were used to represent the spatial boundaries of each module in the workspace (b). The modules (a) extracted during B3 were used to estimate the activation patterns able to describe the movements in all the blocks. We found that the four original synergies were able to represent the data with acceptable median VAF (>80%) in each block (c). The VAF calculated for each block (red errorbars, indicating median and standard error) were significantly higher than the ones obtained using random modules (green errorbars). For each block statistical significance was estimated using Mann-Whitney U test and Bonferroni’s correction.