Frequency Band Analysis of Nonstationary Multivariate Time Series

TL;DR

This paper introduces a data-driven method to identify optimal frequency bands in nonstationary multivariate time series, enhancing the analysis of signals like EEG by capturing their dynamic spectral properties.

Contribution

A novel approach for detecting frequency partition points in multivariate locally stationary time series, improving the summarization of nonstationary spectral information.

Findings

Method accurately identifies frequency change points in simulations.

Effective in characterizing EEG time-varying spectral behavior.

Bootstrap tests reliably detect significant frequency partitions.

Abstract

Information from frequency bands in biomedical time series provides useful summaries of the observed signal. Many existing methods consider summaries of the time series obtained over a few well-known, pre-defined frequency bands of interest. However, these methods do not provide data-driven methods for identifying frequency bands that optimally summarize frequency-domain information in the time series. A new method to identify partition points in the frequency space of a multivariate locally stationary time series is proposed. These partition points signify changes across frequencies in the time-varying behavior of the signal and provide frequency band summary measures that best preserve the nonstationary dynamics of the observed series. An $L_2$ norm-based discrepancy measure that finds differences in the time-varying spectral density matrix is constructed, and its asymptotic properties are derived. New nonparametric bootstrap tests are also provided to identify significant frequency partition points and to identify components and cross-components of the spectral matrix exhibiting changes over frequencies. Finite-sample performance of the proposed method is illustrated via simulations. The proposed method is used to develop optimal frequency band summary measures for characterizing time-varying behavior in resting-state electroencephalography (EEG) time series, as well as identifying components and cross-components associated with each frequency partition point.