An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification-Reference-Cited by-同舟云学术

An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification

Published:2023 Issue: Volume:11 Page:41337-41347
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Xia Min¹^ORCID,Zhang Yangsong¹^ORCID,Wu Yihan¹,Wang Xiuzhu¹

Affiliation:

1. School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, China

Funder

National Natural Science Foundation of China

House-Level Project of Mianyang Central Hospital

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10109090.pdf?arnumber=10109090

Reference44 articles.

1. LSDD-EEGNet: An efficient end-to-end framework for EEG-based depression detection

2. A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography

3. DeprNet: A Deep Convolution Neural Network Framework for Detecting Depression Using EEG

4. Measuring Information Transfer

5. An End-to-End Depression Recognition Method Based on EEGNet

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