Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review

Background: Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees. One of these disorders is depression, a significant factor contributing to the increase in suicide cases worldwide. Consequently, depression has become a significant public health issue globally. Electroencephalogram (EEG) data can be utilized to diagnose mild depression disorder (MDD), offering valuable insights into the pathophysiological mechanisms underlying mental disorders and enhancing the understanding of MDD. Methods: This survey emphasizes the critical role of EEG in advancing artificial intelligence (AI)-driven approaches for depression diagnosis. By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. The survey highlights advancements in EEG preprocessing, feature extraction, and model development, showcasing how these approaches enhance the diagnostic precision, scalability, and automation of depression detection. Results: This survey is distinguished from prior reviews by addressing their limitations and providing researchers with valuable insights for future studies. It offers a comprehensive comparison of ML and DL approaches utilizing EEG and an overview of the five key steps in depression detection. The survey also presents existing datasets for depression diagnosis and critically analyzes their limitations. Furthermore, it explores future directions and challenges, such as enhancing diagnostic robustness with data augmentation techniques and optimizing EEG channel selection for improved accuracy. The potential of transfer learning and encoder-decoder architectures to leverage pre-trained models and enhance diagnostic performance is also discussed. Advancements in feature extraction methods for automated depression diagnosis are highlighted as avenues for improving ML and DL model performance. Additionally, integrating Internet of Things (IoT) devices with EEG for continuous mental health monitoring and distinguishing between different types of depression are identified as critical research areas. Finally, the review emphasizes improving the reliability and predictability of computational intelligence-based models to advance depression diagnosis. Conclusions: This study will serve as a well-organized and helpful reference for researchers working on detecting depression using EEG signals and provide insights into the future directions outlined above, guiding further advancements in the field.