EEG-based high-performance depression state recognition

Z Wang, C Hu, W Liu, X Zhou, X Zhao - Frontiers in Neuroscience, 2024 - frontiersin.org
Depression is a global disease that is harmful to people. Traditional identification methods
based on various scales are not objective and accurate enough. Electroencephalogram …

Explainable depression recognition from eeg signals via graph convolutional network

J Shen, J Chen, Y Ma, Z Cao… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Depression is a prevalent mental disorder that poses significant risks to human health and
social stability. Current methods for diagnosing depression heavily rely on patient …

Generating personalized facial emotions using emotional EEG signals and conditional generative adversarial networks

M Esmaeili, K Kiani - Multimedia Tools and Applications, 2024 - Springer
Facial expressions are one of the most effective and straightforward ways of conveying our
emotions and intentions. Therefore, it is crucial to conduct research aimed at developing a …

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

S Khan, SM Umar Saeed, J Frnda, A Arsalan, R Amin… - Plos one, 2024 - journals.plos.org
Depression is a serious mental health disorder affecting millions of individuals worldwide.
Timely and precise recognition of depression is vital for appropriate mediation and effective …

Application of graph frequency attention convolutional neural networks in depression treatment response

Z Lu, J Wang, F Wang, Z Wu - Frontiers in Psychiatry, 2023 - frontiersin.org
Depression, a prevalent global mental health disorder, necessitates precise treatment
response prediction for the improvement of personalized care and patient prognosis. The …

Amg: A depression detection model with autoencoder and multi-head graph convolutional network

HG Wang, QH Meng, LC Jin, JB Wang… - 2023 42nd chinese …, 2023 - ieeexplore.ieee.org
Depression is a common chronic mental disorder characterized by high prevalence,
recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment …

Graph-Based Electroencephalography Analysis in Tinnitus Therapy

M Awais, K Kassoul, AE Omri, OM Aboumarzouk… - Biomedicines, 2024 - mdpi.com
Tinnitus is the perception of sounds like ringing or buzzing in the ears without any external
source, varying in intensity and potentially becoming chronic. This study aims to enhance …

Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology

W Liu, K Jia, Z Wang - Frontiers in Neuroscience, 2024 - frontiersin.org
Depression has become the prevailing global mental health concern. The accuracy of
traditional depression diagnosis methods faces challenges due to diverse factors, making …

Attention-based multi-semantic dynamical graph convolutional network for eeg-based fatigue detection

H Liu, Q Liu, M Cai, K Chen, L Ma, W Meng… - Frontiers in …, 2023 - frontiersin.org
Introduction Establishing a driving fatigue monitoring system is of utmost importance as
severe fatigue may lead to unimaginable consequences. Fatigue detection methods based …

Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition

X Sun, Y Xu, Y Zhao, X Zheng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Major depressive disorder (MDD) is the most common psychological disease. To improve
the recognition accuracy of MDD, more and more machine learning methods have been …