EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals-Reference-Cited by-同舟云学术

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EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals

Published:2024-09 Issue:9 Volume:28 Page:5168-5179
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Abidi Mustufa Haider¹^ORCID,Moiduddin Khaja¹^ORCID,Ayub Rashid²^ORCID,Mohammed Muneer Khan¹^ORCID,Shankar Achyut³^ORCID,Shiaeles Stavros⁴

Affiliation:

1. Advanced Manufacturing Institute, King Saud University, Riyadh, Saudi Arabia

2. Department of Science Technology and Innovation, King Saud University, Riyadh, Saudi Arabia

3. Department of Cyber Systems Engineering, WMG, University of Warwick, Coventry, U.K.

4. Center for Cybercrime and Economic Crime, University of Portsmouth, Portsmouth, U.K.

Funder

King Saud University, Riyadh

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221020/10666866/10530882.pdf?arnumber=10530882

Reference41 articles.

1. Predicting Depression in Canada by Automatic Filling of Beck’s Depression Inventory Questionnaire

2. Exploring Self-Attention Graph Pooling With EEG-Based Topological Structure and Soft Label for Depression Detection

3. HybridEEGNet: A Convolutional Neural Network for EEG Feature Learning and Depression Discrimination

4. Automated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep Learning

5. Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection

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