[HTML][HTML] Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade
Mental health is a basic need for a sustainable and develo** society. The prevalence and
financial burden of mental illness have increased globally, and especially in response to …
financial burden of mental illness have increased globally, and especially in response to …
Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques
The electroencephalography (EEG) signal is a noninvasive and complex signal that has
numerous applications in biomedical fields, including sleep and the brain–computer …
numerous applications in biomedical fields, including sleep and the brain–computer …
Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals
Abstract The number of Major Depressive Disorder (MDD) patients is rising rapidly these
days following the incidence of COVID‐19 pandemic. It is challenging to detect MDD …
days following the incidence of COVID‐19 pandemic. It is challenging to detect MDD …
Modern methods of diagnostics and treatment of neurodegenerative diseases and depression
N Shusharina, D Yukhnenko, S Botman, V Sapunov… - Diagnostics, 2023 - mdpi.com
This paper discusses the promising areas of research into machine learning applications for
the prevention and correction of neurodegenerative and depressive disorders. These two …
the prevention and correction of neurodegenerative and depressive disorders. These two …
Exploring self-attention graph pooling with EEG-based topological structure and soft label for depression detection
T Chen, Y Guo, S Hao, R Hong - IEEE transactions on affective …, 2022 - ieeexplore.ieee.org
Electroencephalogram (EEG) has been widely used in neurological disease detection, ie,
major depressive disorder (MDD). Recently, some deep EEG-based MDD detection …
major depressive disorder (MDD). Recently, some deep EEG-based MDD detection …
A multi-dimensional graph convolution network for EEG emotion recognition
Due to the changeable, high-dimensional, nonstationary, and other characteristics of
electroencephalography (EEG) signals, the recognition of EEG signals is mostly limited to …
electroencephalography (EEG) signals, the recognition of EEG signals is mostly limited to …
Exploration of EEG-based depression biomarkers identification techniques and their applications: a systematic review
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 …
and suicidal mortality or tendencies. Currently, about 10% of the world population has been …
MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection
T Chen, R Hong, Y Guo, S Hao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Major depressive disorder (MDD) is one of the most common and severe mental illnesses,
posing a huge burden on society and families. Recently, some multimodal methods have …
posing a huge burden on society and families. Recently, some multimodal methods have …
Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2022 - Elsevier
Depression is one of the significant contributors to the global burden disease, affecting
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
[HTML][HTML] A deep learning-based comparative study to track mental depression from EEG data
A Sarkar, A Singh, R Chakraborty - Neuroscience Informatics, 2022 - Elsevier
Background Modern day's society is engaged in commitment-based and time-bound jobs.
This invites tension and mental depression among many people who are not able to cope …
This invites tension and mental depression among many people who are not able to cope …