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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