Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. This variability challenges the sensitivity and specificity of analysis methods. Yet, recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully identify individuals within a cohort and predict their individual traits, yielding the notion of a neural fingerprint. Here, we aimed to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of magnetoencephalography (MEG) resting-state brain activity in 158 participants. The resulting neurophysiological functional connectomes enabled the identification of individuals with similar identifiability rates to fMRI. We also show that individual identification was equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data indicate that identifiability can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: individuals remain identifiable from recordings performed weeks after their baseline reference data was collected. We can anticipate a vast range of diverse applications in personalized, clinical and basic neuroscience of individual differentiation from large-scale neural electrophysiology, in future longitudinal and cross-section studies.

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