Modeling motor learning using heteroskedastic functional principal components analysis

We propose a novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data. We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-...

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Bibliographic Details
Published inJournal of the American Statistical Association Vol. 113; no. 523; pp. 1003 - 1015
Main Authors Backenroth, Daniel, Goldsmith, Jeff, Harran, Michelle D, Cortes, Juan C, Krakauer, John W, Kitago, Tomoko
Format Journal Article
LanguageEnglish
Published United States 2018
Subjects
Kinematic Data
Variance Modeling
Functional Data
Variational Bayes
Motor Control
Probabilistic PCA
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Summary:We propose a novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data. We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-specific random effects. In a setting where principal components are largely invariant across subjects and covariate values, modeling the variance of these scores provides a flexible and interpretable way to explore factors that affect the variability of functional data. Our work is motivated by a novel dataset from an experiment assessing upper extremity motor control, and quantifies the reduction in motion variance associated with skill learning.
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ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.2017.1379403