Modeling Motor Learning Using Heteroscedastic Functional Principal Components Analysis

Daniel Backenroth, Jeff Goldsmith, Michelle D. Harran, Juan C. Cortes, John Krakauer, Tomoko Kitago

Research output: Contribution to journalArticle

Abstract

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 movement variability associated with skill learning. The proposed methods can be applied broadly to understand movement variability, in settings that include motor learning, impairment due to injury or disease, and recovery. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1003-1015
Number of pages13
JournalJournal of the American Statistical Association
Volume113
Issue number523
DOIs
StatePublished - Jul 3 2018

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Keywords

  • Functional data
  • Kinematic data
  • Motor control
  • Probabilistic PCA
  • Variance modeling
  • Variational Bayes

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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