Learning Brain-State Dynamics for Objective Assessment of Stress Resilience via EEG

Stress resilience is a relatively stable psychological trait that reflects an individual's capacity to maintain or rapidly recover adaptive functioning under adversity and sustained pressure. Although EEG signals offer a promising basis for objective resilience assessment beyond self-report measures, estimating stress resilience from EEG remains challenging because resilience is a long-term trait, whereas EEG predominantly reflects real-time, transient neural activity that is entangled with and obscures the slow trait signal. To address this, we propose KoMaNet, the Koopman Manifold Network. It suppresses nuisance at three levels to highlight the underlying trait. A self-supervised state network encodes each clip by imitating an expert descriptor, yielding a stable representation of the brain state. A Koopman dynamic network models the stable dynamics of brain states with a conditioned Koopman operator over the recording context. A manifold subject network classifies subjects by their dynamics on a manifold. We collect the RESIST dataset, comprising overnight-sleep and afternoon-nap paradigms, for stress resilience assessment. KoMaNet achieves AUCs of 0.740 and 0.700 on RESIST Night and RESIST Day, respectively. Model-attribution analyses identify discriminative patterns in occipital delta and frontal theta/alpha/beta bands. On RESIST Night, high-resilience subjects show smaller deviations from stationary dynamics than low-resilience subjects after sleep onset.