model selection for the extraction of movement primitives

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ID: 227658
2013
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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.
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 and the Akaike Information Criterion (AIC)). 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.
Reference Key
endres2013frontiersmodel Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Dominik M Endres;Enrico eChiovetto;Martin eGiese
Journal population health management
Year 2013
DOI
10.3389/fncom.2013.00185
URL
http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00185/full
Keywords
model selection blind source separation neuropsychiatry biological psychiatry

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