Scz-scan: An automated schizophrenia detection system from electroencephalogram signals

G Sahu, M Karnati, A Gupta, A Seal - Biomedical Signal Processing and …, 2023 - Elsevier
Schizophrenia (SCZ) is a severe neurological and physiological syndrome that perverts a
patient's perception of reality. SCZ exhibits several symptoms, including hallucinations …

End-to-end fatigue driving EEG signal detection model based on improved temporal-graph convolution network

H Jia, Z Xiao, P Ji - Computers in Biology and Medicine, 2023 - Elsevier
Fatigue driving is one of the leading causes of traffic accidents, so fatigue driving detection
technology plays a crucial role in road safety. The physiological information-based fatigue …

QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals

G Tasci, MV Gun, T Keles, B Tasci, PD Barua… - Chaos, Solitons & …, 2023 - Elsevier
Background Severe psychiatric disorders, including depressive disorders, schizophrenia
spectrum disorders, and intellectual disability, have devastating impacts on vital life domains …

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 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 …

Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals.

S Zhang, H Wang, Z Zheng, T Liu, W Li… - … Journal of Neural …, 2023 - europepmc.org
Automated detection of depression using Electroencephalogram (EEG) signals has become
a promising application in advanced bioinformatics technology. Although current methods …

Depressive disorder recognition based on frontal EEG signals and deep learning

Y Xu, H Zhong, S Ying, W Liu, G Chen, X Luo, G Li - Sensors, 2023 - mdpi.com
Depressive disorder (DD) has become one of the most common mental diseases, seriously
endangering both the affected person's psychological and physical health. Nowadays, a DD …

AMGCN-L: an adaptive multi-time-window graph convolutional network with long-short-term memory for depression detection

HG Wang, QH Meng, LC Jin… - Journal of Neural …, 2023 - iopscience.iop.org
Objective. Depression is a common chronic mental disorder characterized by high rates of
prevalence, recurrence, suicide, and disability as well as heavy disease burden. An …

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 …