EEG-Based Detection of Depression and Stress: A Comprehensive Review of Signal Processing, Machine Learning, and Clinical Applications

Abstract

Depression and stress are among the most prevalent mental health disorders, posing significant challenges to global healthcare systems. Conventional diagnostic methods often rely on subjective self-reports and clinical evaluations, which may result in delayed or inaccurate assessments. Electroencephalography (EEG) has emerged as a promising biomarker due to its non-invasive, cost-effective, and real-time monitoring capabilities. Recent advances in machine learning and deep learning have further enhanced EEG-based detection, offering improved accuracy and scalability. Studies demonstrate the effectiveness of spiking neural networks for stress detection [1], cloud-based EEG analysis for depression diagnosis [2], and hybrid frameworks such as EEG Mind-Transformer [5] and TSF-MDD [6], which leverage temporal and spatial EEG features. Graph-based representations [4] and explainable AI models [8] have further advanced interpretability and clinical applicability. Despite these achievements, challenges remain in dataset diversity, generalization, privacy, and real-time deployment. This review systematically analyzes EEG-based depression and stress detection methods, covering preprocessing, feature extraction, machine learning, and deep learning approaches, along with benchmarks, challenges, and future directions. The aim is to provide a comprehensive perspective on how EEG can be leveraged to develop reliable, interpretable, and clinically validated systems for mental health monitoring.

Introduction

Depression and chronic stress are major global health issues that significantly impact both mental and physical well-being. Because traditional diagnostic methods like interviews and questionnaires are often subjective and biased, there is a growing need for more objective and automated detection techniques.

EEG (Electroencephalography) has emerged as a key non-invasive tool for this purpose, as it records brain electrical activity and helps identify patterns in different brainwave frequencies linked to depression and stress. Research shows EEG signals, including brain asymmetry and neural oscillations, can effectively distinguish between healthy individuals and those with mental health disorders.

Recent progress in artificial intelligence has greatly improved EEG analysis. Early methods relied on handcrafted features and traditional machine learning models, but modern approaches now use deep learning techniques such as CNNs, LSTMs, spiking neural networks, graph-based models, and transformer architectures. These advanced methods improve accuracy by better capturing spatial and temporal patterns in brain activity. Explainable AI is also being developed to make these systems more transparent and clinically acceptable.

Despite these advances, challenges remain, including limited datasets, differences between individuals, difficulty in real-time deployment, and privacy concerns. Future research is focusing on multimodal systems, wearable EEG devices, edge computing, and privacy-preserving methods like federated learning.

Conclusion

Electroencephalography (EEG) has established itself as a vital tool for understanding the neurophysiological underpinnings of depression and stress. Recent advancements in machine learning and deep learning have significantly improved the accuracy and reliability of EEG-based detection systems. Conventional approaches based on handcrafted features have gradually given way to deep architectures, graph-based learning, spiking neural networks, and transformer models, enabling more robust and automated detection of mental health disorders. Despite these achievements, several challenges persist, including limited and noisy datasets, inter- and intra-subject variability, lack of interpretability in deep models, and barriers to real-world deployment. Ethical issues such as privacy, standardization, and clinical trust also remain unresolved. Addressing these concerns is essential for moving beyond academic research toward clinically viable and socially acceptable solutions. Looking forward, the future of EEG-based depression and stress detection lies in multimodal data fusion, adaptive personalized AI, lightweight edge deployment, explainable AI, and privacy-preserving decentralized learning frameworks. Standardization and cross-disciplinary collaboration will play a central role in translating these technologies into clinical practice. This review underscores that while EEG-based detection systems have made remarkable progress, their true impact will be realized only through interdisciplinary integration, real-world validation, and ethical deployment strategies. Such efforts will pave the way for intelligent, reliable, and patient-centered mental health monitoring systems.

References

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Copyright

Copyright © 2026 Nishchay Kumar, Shivank Soni. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.