Bayesian Mixed-Effects Models for Multilevel Two-way Functional Data: Applications to EEG Experiments

Abstract

In multi-condition EEG experiments, brain activity is recorded as subjects perform various tasks or are exposed to different stimuli. The recorded signals are commonly transformed into time-frequency representations, which often display smooth variations across time and frequency dimensions. These representations are naturally structured as two-way functional data, with experimental conditions nested within subjects. Existing analytical methods fail to jointly account for the data's multilevel structure, functional nature, and dependence on subject-level covariates. To address these limitations, we propose a Bayesian mixed-effects model for two-way functional data that incorporates covariate-dependent fixed effects at the condition level and multilevel random effects. For enhanced model interpretability and parsimony, we introduce a novel covariate-dependent CANDECOMP/PARAFAC (CP) decomposition for the fixed effects, with marginally interpretable time and frequency patterns. We further propose a sparsity-inducing prior for CP rank selection and an efficient algorithm for posterior sampling. The proposed method is evaluated through extensive simulations and applied to EEG data collected to investigate the effects of alcoholism on cognitive processing in response to visual stimuli. Our analysis reveals distinct patterns of time-frequency activity associated with alcoholism, offering new insights into the neural processing differences between subject groups and experimental conditions.