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Depositordc.contributorEbied, Ahmed
Funderdc.contributor.otherThe Egyptian Governmenten_UK
Data Creatordc.creatorEbied, Ahmed
Data Creatordc.creatorEscudero, Javier
Date Accessioneddc.date.accessioned2018-04-19T13:30:07Z
Date Availabledc.date.available2018-04-19T13:30:07Z
Citationdc.identifier.citationEbied, Ahmed; Escudero, Javier. (2018). Matlab Codes for "Evaluation of Matrix Factorisation Approaches for Muscle Synergy Extraction" paper, [software]. University of Edinburgh. School of Engineering. Institute of Digital Communication. https://doi.org/10.7488/ds/2341.en
Persistent Identifierdc.identifier.urihttps://hdl.handle.net/10283/3068
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/2341
Dataset Description (abstract)dc.description.abstractThe muscle synergy concept provides a widely-accepted paradigm to break down the complexity of motor control. In order to identify the synergies, different matrix factorisation techniques have been used in a repertoire of fields such as prosthesis control and biomechanical and clinical studies. However, the relevance of these matrix factorisation techniques is still open for discussion since there is no ground truth for the underlying synergies. Here, we evaluate factorisation techniques and investigate the factors that affect the quality of estimated synergies. We compared commonly used matrix factorisation methods: Principal component analysis (PCA), Independent component analysis (ICA), Non-negative matrix factorization (NMF) and second-order blind identification (SOBI). Publicly available real data were used to assess the synergies extracted by each factorisation method in the classification of wrist movements. Synthetic datasets were utilised to explore the effect of muscle synergy sparsity, level of noise and number of channels on the extracted synergies. Results suggest that the sparse synergy model and a higher number of channels would result in better estimated synergies. Without dimensionality reduction, SOBI showed better results than other factorisation methods. This suggests that SOBI would be an alternative when a limited number of electrodes is available but its performance was still poor in that case. Otherwise, NMF had the best performance when the number of channels was higher than the number of synergies. Therefore, NMF would be the best method for muscle synergy extraction.en_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherUniversity of Edinburgh. School of Engineering. Institute of Digital Communicationen_UK
Relation (Is Referenced By)dc.relation.isreferencedbyhttps://doi.org/10.1016/j.medengphy.2018.04.003en_UK
Relation (Is Referenced By)dc.relation.isreferencedbyAhmed Ebied, Eli Kinney-Lang, Loukianos Spyrou, Javier Escudero. (2018). Evaluation of Matrix Factorisation Approaches for Muscle Synergy Extraction. Medical Engineering and Physics. https://doi.org/10.1016/j.medengphy.2018.04.003.en_UK
Rightsdc.rightsCreative Commons Attribution 4.0 International Public Licenseen
Sourcedc.sourcehttp://ninapro.hevs.ch/node/3en_UK
Subjectdc.subjectEvaluation of Matrix Factorisation Approaches for Muscle Synergy Extractionen_UK
Subjectdc.subjectMuscle synergyen_UK
Subjectdc.subjectIndependent component analysisen_UK
Subjectdc.subjectMatrix factorisationen_UK
Subjectdc.subjectSurface electromyogramen_UK
Subjectdc.subjectNon-negative matrix factorisationen_UK
Subjectdc.subjectPrincipal component analysisen_UK
Subjectdc.subjectsecond-order blind identfication,en_UK
Subject Classificationdc.subject.classificationEngineeringen_UK
Titledc.titleMatlab Codes for "Evaluation of Matrix Factorisation Approaches for Muscle Synergy Extraction" paperen_UK
Alternative Titledc.title.alternativeEvaluation of Matrix Factorisation Approaches for Muscle Synergy Extraction.en_UK
Typedc.typesoftwareen_UK

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