B0999
Title: Comparing populations of high-dimensional spectra
Authors: Robert Krafty - Emory University (United States) [presenting]
Marie Tuft - Sandia National Laboratory (United States)
Fabio Ferrarelli - University of Pittsburgh (United States)
Ori Rosen - University of Texas at El Paso (United States)
Zeda Li - Baruch College City University of New York (United States)
Abstract: Technological advances have led to an increase in the breadth and number of studies that collect high-dimensional time series signals, such as EEG, from multiple groups and whose scientific goal is to understand differences in time series spectra between the groups. Although methods have been proposed for comparing populations of power spectra that are univariate functions of frequency, often referred to as analysis of power (ANOPOW), none exist when time series are high-dimensional and spectra are complex Hermitian matrix-valued functions. We discuss a non-parametric Bayesian approach for ANOPOW with high-dimensional time series. The method models the collection of time series through a novel functional mixed effects factor model that can capture spectral differences between groups while accounting for within-group spectral variability. The approach is motivated by and used to analyze resting-state high-dimensional EEG in patients hospitalized for a first psychotic episode to understand how their electrophysiology differs from that of healthy controls.
