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Deep learning for EEG-based depression detection: A systematic literature review of methods and interpretability
Kashaf Raheem1, Imran Shafi1, Nauman Ahmed1
1College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.
Progress in Neuro-Psychopharmacology & Biological Psychiatry
|August 5, 2026
Summary
Deep learning models show promise for detecting depression using electroencephalography (EEG) brain activity. However, improving model interpretability and addressing data limitations are crucial for clinical use.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Clinical Psychology
Background:
- Depression significantly impacts quality of life, necessitating advanced diagnostic tools.
- Electroencephalography (EEG) offers a non-invasive method for assessing brain activity patterns related to depression.
- Deep learning (DL) models have shown potential in automating depression detection from EEG data.
Purpose of the Study:
- To systematically review literature on EEG-based depression detection using DL and interpretability methods.
- To analyze various DL architectures and interpretability techniques applied to EEG data for depression diagnosis.
- To identify current challenges and future directions for AI-driven depression diagnostics.
Main Methods:
- Systematic literature review of over 100 peer-reviewed studies (2017-2026).
- Focus on studies combining electroencephalography (EEG) data with deep learning (DL) and interpretability methods.
- Analysis of convolutional neural networks (CNNs), long short-term memory networks (LSTMs), hybrid models, SHAP, LIME, and attention mechanisms.
Main Results:
- DL models effectively capture spatio-temporal EEG features for depression detection.
- Interpretability techniques are underutilized in current DL models for EEG-based depression diagnosis.
- Significant challenges remain, including dataset limitations and non-uniform evaluation protocols.
Conclusions:
- While DL excels at EEG feature extraction for depression detection, interpretability needs enhancement for clinical trust.
- Addressing data scarcity, standardization, and transparency is vital for integrating AI into depression diagnostics.
- Future research should focus on reliable, transparent, and clinically applicable AI solutions for depression assessment.