Automatic feature learning model combining functional connectivity network and graph regularization for depression detection

L Yang, X Wei, F Liu, X Zhu, F Zhou - Biomedical Signal Processing and …, 2023 - Elsevier
Depression has become a major health and economic burden worldwide.
Electroencephalography (EEG) data has been used by a growing number of researchers to …

A novel complex network-based graph convolutional network in major depressive disorder detection

X Sun, C Ma, P Chen, M Li, H Wang… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
As a worldwide disease, major depressive disorder (MDD) severely damages patients'
mental health. It is of great significance of detecting MDD accurately in providing necessary …

Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression

S Soni, A Seal, A Yazidi, O Krejcar - Computers in Biology and Medicine, 2022 - Elsevier
Depression is a major depressive disorder characterized by persistent sadness and a sense
of worthlessness, as well as a loss of interest in pleasurable activities, which leads to a …

A Study of Major Depressive Disorder Based on Resting-State Multilayer EEG Function Network

S Sun, S Qu, C Yan, G Luo, X Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Depression is a complex mental disease with its pathological mechanism unclear. To depict
the complete picture of the abnormal information interaction in a depressed brain, this study …

EEG based depression recognition by combining functional brain network and traditional biomarkers

S Sun, H Chen, X Shao, L Liu, X Li… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
This Electroencephalography (EEG)-based research is to explore the effective biomarkers
for depression recognition. Resting-state EEG data were collected from 24 major depressive …

A novel EEG-based graph convolution network for depression detection: incorporating secondary subject partitioning and attention mechanism

Z Zhang, Q Meng, LC Jin, H Wang, H Hou - Expert Systems with …, 2024 - Elsevier
Electroencephalography (EEG) is capable of capturing the evocative neural information
within the brain. As a result, it has been increasingly used for identifying neurological …

A deep learning approach for mild depression recognition based on functional connectivity using electroencephalography

X Li, R La, Y Wang, B Hu, X Zhang - Frontiers in neuroscience, 2020 - frontiersin.org
Early detection remains a significant challenge for the treatment of depression. In our work,
we proposed a novel approach to mild depression recognition using …

EEG-based mild depressive detection using feature selection methods and classifiers

X Li, B Hu, S Sun, H Cai - Computer methods and programs in biomedicine, 2016 - Elsevier
Background and objective Depression has become a major health burden worldwide, and
effectively detection of such disorder is a great challenge which requires latest technological …

EEG based depression recognition using improved graph convolutional neural network

J Zhu, C Jiang, J Chen, X Lin, R Yu, X Li… - Computers in Biology and …, 2022 - Elsevier
Depression is a global psychological disease that does serious harm to people. Traditional
diagnostic method of the doctor-patient communication, is not objective and accurate …

Exploration of EEG-based depression biomarkers identification techniques and their applications: a systematic review

A Dev, N Roy, MK Islam, C Biswas, HU Ahmed… - IEEE …, 2022 - ieeexplore.ieee.org
Depression is the most common mental illness, which has become the major cause of fear
and suicidal mortality or tendencies. Currently, about 10% of the world population has been …