A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data

Document Type

Article

Publication Date

9-1-2023

Abstract

Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activity include interactions among unknown sources, low signal-to-noise ratio, and substantial between-subject heterogeneity. In this work, we propose a state space model that jointly analyzes multichannel EEG signals and learns dynamics of different sources corresponding to brain cortical activity. Our model borrows strength from spatially correlated measurements and uses low-dimensional latent states to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case-control study of alcoholism and reveal significant attenuation of brain activity in response to visual stimuli in alcoholic subjects compared to healthy controls.

Identifier

85138301083 (Scopus)

Publication Title

Biometrics

External Full Text Location

https://doi.org/10.1111/biom.13742

e-ISSN

15410420

ISSN

0006341X

PubMed ID

36004670

First Page

2444

Last Page

2457

Issue

3

Volume

79

Grant

GM124104

Fund Ref

National Institutes of Health

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