Comprehensive Summary
This minireview by Wang et al. examines the potential of portable electroencephalography (EEG) as an objective tool for early detection of depression. The authors systematically reviewed 45 studies selected from over 700 screened publications, focusing on how portable EEG captures neural oscillations linked to depressive symptoms such as low mood, anhedonia, and excessive guilt. Findings showed consistent EEG abnormalities, including reduced frontal alpha activity, increased beta activity, diminished P300 amplitude, and lower signal complexity in depression patients. When paired with machine learning and deep learning algorithms, portable EEG systems achieved classification accuracies above 90% in some trials, with long short-term memory models reaching nearly 98%. Despite these promising results, most studies were conducted in laboratory settings with small, homogeneous samples, and the absence of standardized protocols and large-scale clinical validation remains a major limitation.
Outcomes and Implications
Portable EEG offers a promising alternative to current depression screening methods, which rely heavily on subjective self-reports like the PHQ-9. By providing objective neurophysiological biomarkers, it could allow earlier and more accurate diagnosis, especially in primary care or community settings where psychiatric expertise may be limited. Clinically, portable EEG could be integrated into mobile health platforms to enable at-home monitoring, remote consultations, and even personalized interventions. However, regulatory approval will require rigorous validation through large-scale, multi-center clinical trials and demonstration of cost-effectiveness compared to existing tools. While widespread implementation is still several years away, continued advances in sensor technology, algorithm optimization, and multimodal integration position portable EEG as a potentially transformative tool in psychiatry and mental health care.