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Abstract The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age (BA)," which can be compared to chronological age to reflect the degree of deviation from normal aging. Here, we develop an interpretable machine learning model to predict BA based on 2 large sleep EEG data sets: the Massachusetts General Hospital (MGH) sleep lab data set (N = 2532; ages 18–80); and the Sleep Heart Health Study (SHHS, N = 1974; ages 40–80). The model obtains a mean absolute deviation of 7.6 years between BA and chronological age (CA) in healthy participants in the MGH data set. As validation, a subset of SHHS containing longitudinal EEGs 5.2 years apart shows an average of 5.4 years increase in BA. Participants with significant neurological or psychiatric disease exhibit a mean excess BA, or "brain age index" (BAI = BA-CA) of 4 years relative to healthy controls. Participants with hypertension and diabetes have a mean excess BA of 3.5 years. The findings raise the prospect of using the sleep EEG as a potential biomarker for healthy brain aging. Highlights • Brain age based on two large sleep EEG datasets (N MGH = 2532 and N SHHS = 1974). • Brain age tracks chronological age at the population level. • Chronological age is predicted with mean absolute deviation of 7.6 years in healthy participants. • Significant neurological and psychiatric diseases lead to a 4-year increase in brain age. • Interpretation using age norms derived from matched brain age and chorological age. [ABSTRACT FROM AUTHOR]