Oscillatory biomarkers for differentiating between unipolar depression and bipolar disorder using resting‐state electroencephalography

Abstract The substantial symptomatic overlap between unipolar depression, primarily major depressive disorder (MDD), and bipolar disorder complicates clinical diagnosis, underscoring the critical need for objective biomarkers. Resting‐state electroencephalography (rsEEG) holds promise for differentiating these two conditions due to its non‐invasive nature, high temporal resolution, and clinical feasibility. In this study, we reviewed rsEEG studies that explored spectral power, functional connectivity, nonlinear dynamics, machine learning (ML) applications, and animal models. The key discriminative features identified included specific spatial patterns in the high alpha band, distinctions between periodic and aperiodic components, genetically influenced alpha coherence, and reduced central–parietal alpha phase variability in patients with MDD. While ML models integrating these diverse features achieved high classification accuracy, findings for frequency power and network metrics remained inconsistent across studies. Current limitations, however, include substantial methodological heterogeneity, small sample sizes, a lack of longitudinal data, poor model generalizability, and minimal supporting evidence from animal models. We conclude by outlining these key potential markers, identifying current research gaps, and proposing future directions, specifically emphasizing the adoption of standardized pipelines, large multicenter longitudinal studies, advanced ML techniques, and the critical integration of genetic and animal findings for the development of reliable rsEEG‐based diagnostic tools.