Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets.

By Matthew C. Tresch, V. Cheung, Andrea d'Avella. Journal of Neurophysiology, 2006

817 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/29e1bcd51f4181a1d4b85bce8b1285dcc76b3ac2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Mark L. Latash
2. Francesco Lacquaniti
3. John P. Scholz
4. Gregor Schöner
5. Yuri P. Ivanenko
6. V. Cheung
7. Andrea d'Avella
8. Emilio Bizzi

In Social Psychology

1. Mark L. Latash
2. Francesco Lacquaniti
3. V. Cheung
4. Emilio Bizzi
5. Laure Fernandez
6. Alessandro Portone
7. Yuri P. Ivanenko
8. Richard Poppele

Citations in the corpus

• Control of Fast-Reaching Movements by Muscle Synergy Combinations (2006, methodology): “This procedure is analogous to the “scree test” commonly used for the selection of the number of factors in factor analysis (Cattell, 1966) and has been shown to be adequate for identifying the correct number of non-negative synchronous muscle synergies (Tresch et al., 2006).”
• Toward a new theory of motor synergies. (2007, methodology): “Combinations of original variables identified with PCA and similar procedures are sometimes considered basic synergies that can be combined to form functional synergies for particular behaviors (e.g., Tresch et al., 2006).”
• Motor patterns in human walking and running. (2006, methodology): “Different statistical methods have been developed to assess a linear decomposition of the original set of data based on different assumptions (Tresch et al. 2006).”
• Muscle synergy patterns as physiological markers of motor cortical damage (2012, background): “Importantly, an earlier study has shown that different linear factorization algorithms (except principal component analysis) produced similar synergies in both simulated and experimental datasets (16).”
• The neural origin of muscle synergies (2013, methodology): “As Tresch et al. (2006) have shown, most of the algorithms, with the exception of principal component analysis (Jolliffe,2002), perform comparably in simulated and experimental data sets.”
• CEINMS: a toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks (2015, neutral): “2) (d'Avella and Tresch, 2002; Neptune et al., 2009; Sartori et al., 2013; Tresch et al., 2006).”