ArticleFrontiers in computational neuroscience2013
Model selection for the extraction of movement primitives.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model.Computational brain & behavior · 2024Article
- Comparison of linear combination modeling strategies for edited magnetic resonance spectroscopy at 3 T.NMR in biomedicine · 2022Article
- Toward a unifying framework for the modeling and identification of motor primitives.Frontiers in computational neuroscience · 2022Article
- Muscle Synergies in Parkinson's Disease.Sensors (Basel, Switzerland) · 2020Review
- Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations.Entropy (Basel, Switzerland) · 2018Article
- Editorial: Modularity in motor control: from muscle synergies to cognitive action representation.Frontiers in computational neuroscience · 2015Article
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Authors and funding
3 authors.
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Abstract
A wide range of blind source separation methods have been used in motor control research for the extraction of movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA), independent component analysis (ICA), anechoic demixing, and the time-varying synergy model (d'Avella and Tresch, 2002). However, choosing the parameters of these models, or indeed choosing the type of model, is often done in a heuristic fashion, driven by result expectations as much as by the data. We propose an objective criterion which allows to select the model type, number of primitives and the temporal smoothness prior. Our approach is based on a Laplace approximation to the posterior distribution of the parameters of a given blind source separation model, re-formulated as a Bayesian generative model. We first validate our criterion on ground truth data, showing that it performs at least as good as traditional model selection criteria [Bayesian information criterion, BIC (Schwarz, 1978) and the Akaike Information Criterion (AIC) (Akaike, 1974)]. Then, we analyze human gait data, finding that an anechoic mixture model with a temporal smoothness constraint on the sources can best account for the data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.