6th Edition of Neurology World Conference 2026

Abstract

Depression is a common mental disorder that impacts millions of people worldwide. However, its diagnosis is difficult owing to subjective testing. Although quantitative electroencephalography (EEG) has been investigated as a promising diagnostic tool for depression, the associated results have proven contradictory. The current study seeks to determine whether the alpha/beta, alpha/theta, and theta/beta ratios can be biological markers of depression. We used open-access EEG data to investigate these power ratios in 46 patients with depression and 75 healthy individuals. Decreased anterior frontal, frontal, central, parietal, occipital, and temporal alpha/beta ratios and decreased central and parietal theta/beta ratios were observed in the depression group. All ratios could effectively differentiate depression from healthy individuals. In particular, the central, frontal, and parietal alpha/beta ratios exhibited high discriminating ability. No significant differences were observed in the alpha/theta ratio. Depression, stress level, and the probability of depression increased with a decrease in the central alpha/beta ratio. Moreover, machine learning analysis revealed that the alpha/beta ratio was the most effective for diagnosing depression among the three EEG power ratios. These findings lay the foundation for future studies to develop more effective and efficient methods for diagnosing depression.