Depression Detection Through EEG Signal Analysis: A Convolutional Autoencoder Deep Learning Model

Depression is a debilitating and enervating mental health disorder that requires attention for necessitating accurate and efficient diagnostic techniques. Developments in deep learning and neurophysiological data analysis have enabled the use of EEG signals for binary depression classification. In t...

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Bibliographic Details
Published in:2025 IEEE 26th International Conference of Young Professionals in Electron Devices and Materials (EDM) pp. 1820 - 1825
Main Authors: Firoz, Neda, Aksyonov, Sergey Vladimirovich, Berestneva, Olga Grigorievna, Savostyanov, Alexander
Format: Conference Proceeding
Language:English
Published: IEEE 27.06.2025
Subjects:
Accuracy
Autoencoders
Brain modeling
Computational modeling
Convolution
Convolutional Autoencoders
Deep learning
Depression
EEG signals
Electroencephalography
Feature extraction
ICBrainDB
Machine learning
Mental health
ISSN:2325-419X
Online Access:Get full text
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Summary:Depression is a debilitating and enervating mental health disorder that requires attention for necessitating accurate and efficient diagnostic techniques. Developments in deep learning and neurophysiological data analysis have enabled the use of EEG signals for binary depression classification. In this study, a novel approach is introduced that utilizes Convolutional Autoencoders for feature extraction from EEG signals, to enhance feature representation for classification of depression. To our knowledge, this is the first study utilizing the ICBrainDB dataset, which includes EEG test results and psychological questionnaire responses from over 1,000 participants across various regions of Russia. A series of experiments were carried out to evaluate the classification performance of both traditional machine learning and deep learning approaches in predicting depression using this novel dataset. The findings demonstrate that incorporating EEG feature sets extracted through CAE encodings significantly enhances classification accuracy. Specifically, the Random Forest and CNN models achieved impressive classification accuracies of 98.31% and 99.31%, respectively, in distinguishing individuals with depression from healthy controls. This study contributes to the expanding field of computational psychiatry by introducing a robust, data-driven framework for depression prediction, fostering the development of more reliable and automated mental health assessments.
ISSN:2325-419X
DOI:10.1109/EDM65517.2025.11096865