Depression Detection Based on Electroencephalography Using a Hybrid Deep Neural Network CNN-GRU and MRMR Feature Selection

2026-01-16

AI Summary

This study addresses the limitations of traditional depression diagnosis, which relies heavily on subjective self-reports and lacks objective biomarkers, by proposing an automated detection method based on electroencephalogram (EEG) signals. The approach employs a hybrid deep neural network architecture combining convolutional neural networks (CNNs) and gated recurrent units (GRUs) to jointly capture spatiotemporal features from EEG data. To enhance model discriminability, the minimum redundancy maximum relevance (mRMR) algorithm is integrated for efficient feature selection. Experimental results demonstrate that the proposed method achieves a high accuracy of 98.74% in identifying depressive states, highlighting its strong diagnostic precision and potential for clinical translation. This work thus offers a promising and objective technical pathway for the auxiliary diagnosis of depression.

Technology Category

Knowledge Representation and Reasoning: Diagnosis and Abductive ReasoningMachine Learning: Dimensionality Reduction/Feature SelectionCognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasetsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methodsSearch and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This study investigates the detection and classification of depressive and non-depressive states using deep learning approaches. Depression is a prevalent mental health disorder that substantially affects quality of life, and early diagnosis can greatly enhance treatment effectiveness and patient care. However, conventional diagnostic methods rely heavily on self-reported assessments, which are often subjective and may lack reliability. Consequently, there is a strong need for objective and accurate techniques to identify depressive states. In this work, a deep learning based framework is proposed for the early detection of depression using EEG signals. EEG data, which capture underlying brain activity and are not influenced by external behavioral factors, can reveal subtle neural changes associated with depression. The proposed approach combines convolutional neural networks (CNNs) and gated recurrent units (GRUs) to jointly extract spatial and temporal features from EEG recordings. The minimum redundancy maximum relevance (MRMR) algorithm is then applied to select the most informative features, followed by classification using a fully connected neural network. The results demonstrate that the proposed model achieves high performance in accurately identifying depressive states, with an overall accuracy of 98.74%. By effectively integrating temporal and spatial information and employing optimized feature selection, this method shows strong potential as a reliable tool for clinical applications. Overall, the proposed framework not only enables accurate early detection of depression but also has the potential to support improved treatment strategies and patient outcomes.

Problem

• Depression Detection
• Electroencephalography
• Objective Diagnosis
• Mental Health Disorder
• Early Detection

Innovation

• CNN-GRU
• MRMR feature selection
• EEG-based depression detection
• deep learning
• temporal-spatial feature extraction