Scalp EEG reveals functional dissociable aperiodic timescales in divergence of mental health

Understanding the divergence of mental health requires recognizing the brain’s real-time information processing. For example, aperiodic neural activity characterizing intrinsic brain dynamics is increasingly used as a biomarker in mental health, yet how its temporal complexity relates to mental deviations in ageing and disorders remains unknown. Here, using resting-state electroencephalography from approximately 1,700 participants across healthy, neurological and psychiatric disorders and chronic-pain cohorts, we show that the knee-like frequency structure recovered from individual spectra of neural activity converges into two reproducible, population-level temporal components. These slow and fast aperiodic timescales showed distinct functional profiles: the slow component remained stable across all states of mental health, whereas the fast component increased with healthy ageing, decreased in mental disorders and was unchanged in chronic pain. These findings establish that scalp EEG preserves functionally dissociable aperiodic timescales, possibly reflecting body-mind interactions, offering scalable, non-invasive temporal markers for quantifying divergent states of mental health.