Speaker
Description
Resting-state electrophysiological activity encodes stable, individual-specific neural fingerprints that relate to cognitive emotion regulation. While such fingerprints are well established in MEG and fMRI, their stability and behavioral relevance in EEG remain unclear. Here, we examine sensor-level resting-state EEG spectral fingerprints in a deeply phenotyped cohort (N = 121), focusing on an initial subset (N = 10). EEG data were segmented into 60 non-overlapping epochs, and power spectral density features were used to construct individual fingerprints. These fingerprints showed high within-subject stability (r̄ = 0.881), exceeding between-subject similarity (r̄ = 0.589), demonstrating reliable individual differentiation. Fingerprint power metrics were systematically associated with cognitive reappraisal performance, particularly across delta, theta, alpha, and beta bands. These findings establish resting-state EEG fingerprints as stable, behaviorally meaningful markers of individual differences in cognitive emotion regulation.