A Systematic Review of EEG-based Machine Intelligence Algorithms for Depression Diagnosis, and Monitoring

2025-03-25

AI Summary

Depression diagnosis has long relied on subjective clinical assessments, suffering from insufficient objectivity and diagnostic delay. This study conducts a systematic review of 139 EEG-based machine learning studies published between 1985 and 2024, adhering to the PRISMA framework. It establishes, for the first time, a comprehensive methodological taxonomy spanning data acquisition, artifact correction, time-frequency feature extraction, model architectures (e.g., SVM, CNN, LSTM), and multicenter validation. Key bottlenecks identified include poor model generalizability, weak cross-device robustness, and overreliance on small, single-center datasets for peak reported accuracy (~94%). To bridge the translational gap, we propose three clinically oriented pathways: (1) lightweight deployment on wearable platforms, (2) enhanced model interpretability, and (3) real-time analytical frameworks. The work delivers a standardized classification scheme and a methodology evaluation matrix, providing a rigorous, evidence-based foundation for EEG-driven intelligent depression diagnosis.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Depression disorder is a serious health condition that has affected the lives of millions of people around the world. Diagnosis of depression is a challenging practice that relies heavily on subjective studies and, in most cases, suffers from late findings. Electroencephalography (EEG) biomarkers have been suggested and investigated in recent years as a potential transformative objective practice. In this article, for the first time, a detailed systematic review of EEG-based depression diagnosis approaches is conducted using advanced machine learning techniques and statistical analyses. For this, 938 potentially relevant articles (since 1985) were initially detected and filtered into 139 relevant articles based on the review scheme 'preferred reporting items for systematic reviews and meta-analyses (PRISMA).' This article compares and discusses the selected articles and categorizes them according to the type of machine learning techniques and statistical analyses. Algorithms, preprocessing techniques, extracted features, and data acquisition systems are discussed and summarized. This review paper explains the existing challenges of the current algorithms and sheds light on the future direction of the field. This systematic review outlines the issues and challenges in machine intelligence for the diagnosis of EEG depression that can be addressed in future studies and possibly in future wearable technologies.

Problem

• Reviewing EEG-based machine learning for depression diagnosis
• Analyzing algorithms and statistical methods in EEG depression studies
• Identifying challenges in EEG depression diagnosis for future research

Innovation

• EEG-based machine learning for depression diagnosis
• Systematic review using PRISMA and statistical analyses
• Future wearable technologies for EEG monitoring