EEG brain scan showing alpha wave activity in the left central cortex, potentially indicating depressive symptoms.

Unlocking the Mind: Can EEG Scans Detect Early Signs of Depression?

Rhea Morgan in Health & Wellness December 2025 • 4 min read.

"New research explores how EEG brain scans might offer a non-invasive way to identify neurophysiological markers of depressive symptoms in young adults, potentially revolutionizing early diagnosis and intervention."

Depression is a pervasive and debilitating condition affecting millions worldwide, with a significant impact on young adults. Early detection is crucial, but often challenging due to the stigma surrounding mental health and the lack of readily available, objective diagnostic tools. Traditional methods rely heavily on self-reporting and clinical assessments, which can be subjective and influenced by various factors.

Electroencephalography (EEG), a non-invasive technique that measures brain electrical activity, has emerged as a promising tool for exploring the neurophysiological underpinnings of depression. EEG offers a window into the brain's dynamic processes, potentially revealing subtle changes that may precede the onset of full-blown depressive episodes. It provides a relatively accessible and cost-effective means of assessing brain function, making it attractive for widespread screening and monitoring.

A recent study published in the Journal of Clinical Neuroscience delved into the potential of quantitative EEG (qEEG) to identify neurophysiological correlates of depressive symptoms in young adults. This research sought to determine if specific brainwave patterns could differentiate between individuals experiencing depressive symptoms and their euthymic (healthy mood) counterparts, offering a new avenue for early detection and intervention.

Decoding Brainwaves: How EEG Reveals Depression's Footprint

EEG brain scan showing alpha wave activity in the left central cortex, potentially indicating depressive symptoms.

The study, conducted by Lee et al., involved 100 participants, half of whom exhibited depressive symptoms based on standardized screening tools (PHQ-9 and DASS-21). All participants underwent a 32-channel EEG assessment, capturing their brain electrical activity while at rest with eyes closed. Researchers then analyzed the EEG data to identify differences in brainwave patterns between the two groups.

The analysis focused on different frequency bands, including delta, theta, alpha (low and high), and beta, each associated with distinct brain states and cognitive processes. Researchers employed statistical methods, including logistic regression and receiver operating characteristic (ROC) curve analysis, to determine which EEG measures could reliably discriminate between the depressed and euthymic groups.

The key findings of the study highlighted the significance of alpha and beta power, particularly in the left central cortex (C3):

High-Alpha Power: Reduced high-alpha power over the left central cortex (C3) was the most significant predictor of depressive symptoms.
Beta Power: Beta power was significantly reduced over left central areas in the depressive group.
Discriminative Value: High-alpha and beta power in the C3 region demonstrated a reliable ability to differentiate between the depressive and euthymic groups.

These results suggest that individuals experiencing depressive symptoms exhibit altered brainwave patterns, particularly in regions associated with emotional processing and cognitive control. The decreased high-alpha power observed in the left central cortex may reflect reduced cortical inhibition or altered activity within the default mode network, a brain network implicated in self-referential thought and emotional regulation.

EEG as a Future Tool: Implications and Next Steps

This study offers valuable insights into the potential of EEG as a non-invasive tool for identifying neurophysiological markers of depressive symptoms in young adults. While further research is needed to validate these findings and explore their clinical utility, the results suggest that EEG could play a crucial role in early detection and intervention efforts. Future studies should investigate the longitudinal changes in EEG patterns associated with the development and progression of depression, as well as the potential for EEG-guided interventions, such as neurofeedback, to improve mood and cognitive function. Combining EEG with other diagnostic and treatment modalities could pave the way for more personalized and effective approaches to managing depression in young adults.

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This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.jocn.2017.09.030, Alternate LINK

Title: Neurophysiological Correlates Of Depressive Symptoms In Young Adults: A Quantitative Eeg Study

Subject: Physiology (medical)

Journal: Journal of Clinical Neuroscience

Publisher: Elsevier BV

Authors: Poh Foong Lee, Donica Pei Xin Kan, Paul Croarkin, Cheng Kar Phang, Deniz Doruk

Published: 2018-01-01

Everything You Need To Know

What is the role of EEG in detecting early signs of depression?

Electroencephalography (EEG) is a non-invasive technique used to measure brain electrical activity. Research suggests that EEG can identify neurophysiological markers of depressive symptoms, potentially enabling early detection. The study by Lee et al. used quantitative EEG (qEEG) to find specific brainwave patterns associated with depressive symptoms in young adults. This method offers a promising approach to overcome limitations of traditional methods, like self-reporting, that can be subjective. By analyzing brainwave patterns, EEG could help identify individuals at risk of developing depression before the onset of severe symptoms.

How did the study by Lee et al. use EEG to identify depressive symptoms?

The study involved 100 participants, split into two groups: those with depressive symptoms and euthymic (healthy mood) individuals. All participants underwent a 32-channel EEG assessment, capturing brain electrical activity while at rest with eyes closed. Researchers then analyzed the EEG data, focusing on different frequency bands such as delta, theta, alpha (low and high), and beta. They used statistical methods, like logistic regression and ROC curve analysis, to pinpoint which EEG measures could reliably distinguish between the two groups. The study specifically looked at high-alpha and beta power in the left central cortex (C3) and found that these measures could differentiate between individuals experiencing depressive symptoms and their euthymic counterparts.

What specific brainwave patterns did the study identify as indicators of depression?

The study found that reduced high-alpha power and reduced beta power in the left central cortex (C3) were the most significant predictors of depressive symptoms. High-alpha power, often associated with a relaxed and alert state, was found to be decreased in individuals with depressive symptoms. Beta power was also significantly reduced in the same area. These findings suggest that changes in these specific brainwave patterns may be associated with altered emotional processing and cognitive control in individuals experiencing depression. Specifically, the decreased high-alpha power may reflect reduced cortical inhibition or altered activity within the default mode network.

What are the implications of using qEEG for early detection of depression?

The potential of qEEG for early detection lies in its ability to provide an objective and non-invasive method for identifying neurophysiological markers of depressive symptoms. This approach can help circumvent the subjectivity and limitations of traditional diagnostic tools. Early detection is crucial because it allows for timely intervention and treatment, potentially preventing the condition from worsening. It also offers a means of screening for individuals at risk and monitoring the effectiveness of treatments. The study by Lee et al. suggests that specific brainwave patterns can be used to differentiate between individuals with and without depressive symptoms, which could revolutionize the way depression is diagnosed and managed in young adults.

What are the next steps for research in using EEG to diagnose and treat depression?

Future research should aim to validate the findings of the study by Lee et al. and explore their clinical utility. This includes longitudinal studies to track changes in EEG patterns over time and assess how they relate to the development and progression of depression. Another key area is investigating the potential for EEG-guided interventions, such as neurofeedback, to improve mood and cognitive function. Combining EEG with other diagnostic methods and treatment approaches could lead to more personalized and effective management of depression. Researchers need to determine how EEG can be integrated into clinical practice to improve early detection, diagnosis, and treatment outcomes for young adults struggling with depression.