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

Qinxia Wang, Ji Meng Loh, Xiaofu He, Yuanjia Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2444-2457
Number of pages14
JournalBiometrics
Volume79
Issue number3
DOIs
StatePublished - Sep 2023

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics

Keywords

  • Kalman filter
  • alcoholism
  • latent sources
  • multichannel EEG signals
  • patient heterogeneity
  • state space models

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