Personalized EEG models of epilepsy and non-epilepsy show similar predictive fit but markedly different learned latent dependency structure
Using frozen representations from a lightweight CNN–Transformer EEG foundation model pretrained on TUEG, the study maps TUEP segments into a shared latent-state space and fits sparse multinomial logistic transition distributions independently per subject to obtain personalized latent transition-dependency graphs; it finds that epilepsy and non-epilepsy subjects are comparably predictable within-subject yet epilepsy models retain substantially denser learned dependency structure, with graph-derived features retaining moderate group discrimination.