Biometrics. 2022 Aug 25. doi: 10.1111/biom.13742. Online ahead of print.
ABSTRACT
Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a non-invasive 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 multi-channel 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. This article is protected by copyright. All rights reserved.
PMID:36004670 | DOI:10.1111/biom.13742
