Using principal component analysis to reduce complex datasets produced by robotic technology in healthy participants.

Journal: Journal of neuroengineering and rehabilitation
Published Date: Jul 31, 2018

Abstract

BACKGROUND: The KINARM robot produces a granular dataset of participant performance metrics associated with proprioceptive, motor, visuospatial, and executive function. This comprehensive battery includes several behavioral tasks that each generate 9 to 20 metrics of performance. Therefore, the entire battery of tasks generates well over 100 metrics per participant, which can make clinical interpretation challenging. Therefore, we sought to reduce these multivariate data by applying principal component analysis (PCA) to increase interpretability while minimizing information loss.

Authors

  • Michael D Wood
    Centre for Neuroscience Studies, Queen's University, Botterell Hall, 18 Stuart St, Kingston, ON, Canada.
  • Leif E R Simmatis
    Centre for Neuroscience Studies, Queen's University, Botterell Hall, 18 Stuart St, Kingston, ON, Canada.
  • J Gordon Boyd
    Centre for Neuroscience Studies, Queen's University, Botterell Hall, 18 Stuart St, Kingston, ON, Canada.
  • Stephen H Scott
    Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada. steve.scott@queensu.ca.
  • Jill A Jacobson
    Department of Psychology, Queen's University, 318 Craine Hall, 62 Arch Street, Kingston, ON, K7L 3N6, Canada. jill.jacobson@queensu.ca.

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