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Interpretable Principal Components Analysis for Multilevel Multivariate Functional Data, with Application to EEG Experiments by Zhang, Jun; Siegle, Greg J; D'Andrea, Wendy; Krafty, Robert T is a scholarly article available to read on EtoBox.

What is Interpretable Principal Components Analysis for Multilevel Multivariate Functional Data, with Application to EEG Experiments about?

Many studies collect functional data from multiple subjects that have both multilevel and multivariate structures. An example of such data comes from popular neuroscience experiments where participants' brain activity is recorded using modalities such as EEG and summarized as power within multiple time-varying frequency bands within multiple electrodes, or brain regions. Summarizing the joint variation across multiple frequency bands for both whole-brain variability between subjects, as well as location-variation within subjects, can help to explain neural reactions to stimuli. This article introduces a novel approach to conducting interpretable principal components analysis on multilevel multivariate functional data that decomposes total variation into subject-level and replicate-within-subject-level (i.e. electrode-level) variation, and provides interpretable components that can be both sparse among variates (e.g. frequency bands) and have localized support over time within each frequency band. The sparsity and localization of components is achieved by solving an innovative rank-one based convex optimization problem with block Frobenius and matrix $L_1$-norm based penalties. The

Author

Zhang, Jun; Siegle, Greg J; D'Andrea, Wendy; Krafty, Robert T

Published

2019

Language

EN