īizzi E, Cheung VC (2013) The neural origin of muscle synergies. īesomi M, Hodges PW, Clancy EA, Van Dieën J, Hug F, Lowery M, Merletti R, Søgaard K, Wrigley T, Besier T, Carson RG, Disselhorst-Klug C, Enoka RM, Falla D, Farina D, Gandevia S, Holobar A, Kiernan MC, McGill K, Perreault E, Rothwell JC, Tucker K (2020) Consensus for experimental design in electromyography (CEDE) project: amplitude normalization matrix. IEEE, pp 3415–3418īerger DJ, Masciullo M, Molinari M, Lacquaniti F, d’Avella A (2020) Does the cerebellum shape the spatiotemporal organization of muscle patterns? J Neurophysiol, Insights from subjects with cerebellar ataxias. In: 1995 International conference on acoustics, speech, and signal processing, 1995. īell AJ, Sejnowski TJ (1995) Blind separation and blind deconvolution: an information-theoretic approach. īarroso FO, Torricelli D, Moreno JC, Taylor J, Gomez-Soriano J, Bravo-Esteban E, Piazza S, Santos C, Pons JL (2014) Shared muscle synergies in human walking and cycling. Front Comput Neurosci 11:78īarradas VR, Kutch JJ, Kawase T, Koike Y, Schweighofer N (2020) When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance. īanks CL, Pai MM, McGuirk TE, Fregly BJ, Patten C (2017) Methodological choices in muscle synergy analysis impact differentiation of physiological characteristics following stroke. Īllen JL, McKay JL, Sawers A, Hackney ME, Ting LH (2017) Increased neuromuscular consistency in gait and balance after partnered, dance-based rehabilitation in Parkinson’s disease. To select a number, criteria based on noise estimates, reliability of analysis results, or functional outcomes of the synergies provide interesting substitutes to criteria solely based on variance thresholds.Ījiboye AB, Weir RF (2009) Muscle synergies as a predictive framework for the EMG patterns of new hand postures. The number of synergies represents either the dimension of the spatial structure or the number of independent temporal patterns, and we observed that these two aspects are often mixed in the analysis. The concurrent use of various synergy formulations-spatial, temporal and spatio-temporal synergies- should be encouraged. In order to robustly identify synergies, experiments should be performed so that the groups of muscles that would potentially form a synergy are activated with a sufficient level of activity, ensuring that the synergy subspace is fully explored. In particular, attention should be paid to EMG amplitude normalization, baseline noise removal or EMG filtering which may diminish or increase the signal-to-noise ratio of the EMG signal and could have major effects on synergy estimates. While synergy analysis appears to be a robust technique, it remain a statistical tool and is, therefore, sensitive to the amount and quality of input data (EMGs). Recent studies have pointed out variability in outcomes associated with the different methodological options available and there was a need to clarify several aspects of the analysis methodology. The analysis uses dimensionality reduction techniques to identify regularities in spatial, temporal or spatio-temporal patterns of multiple muscle activation. Muscle synergy analysis is increasingly used in domains such as neurosciences, robotics, rehabilitation or sport sciences to analyze and better understand motor coordination.
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