Interpretable principal component analysis for multilevel multivariate functional data.

ABSTRACT: 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 electroencephalography 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

SUBMITTER: Zhang J 

PROVIDER: S-EPMC10102903 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature