A Latent State Space Model for Estimating Brain Dynamics from Electroencephalogram (EEG) Data

• Activity Number: 461 - Perils and Opportunities for Analyzing Biological, Behavioral, and Digital Phenotypes of Mental Functions
• Type: Invited
• Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Mental Health Statistics Section
• Author(s): Yuanjia Wang* and Qinxia Wang and Jimeng Loh and Xiaofu He
• Companies: Columbia University and Novartis and NJIT and Columia University
• Keywords: State space models; Multi-channel EEG signals; Alcoholism; atient heterogeneity; Latent sources; Kalman filter

Abstract

Modern neuroimaging technologies have substantially advanced the measurement of brain activities.Electroencephalogram (EEG) as a non-invasive neuroimaging technique measures changes in electrical voltage on thescalp induced by cortical activities. With its high temporal resolution, EEG has emerged as an increasingly useful toolto study brain connectivity. Challenges with modeling EEG signals of complex brain activities include interactions among unknown sources, low signal-to-noise ratio and substantial between-subject heterogeneity. We propose a state space model that jointly analyzes multi-channel EEG signals and learns dynamics of different sources corresponding to brain cortical activities. Our model borrows strength from spatially correlated measurements anduses low-dimensional latent sources 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 groups. We apply the developed approach to a case-control study of subjects at risk of alcoholism.