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 …

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 …

Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification

B Zhang, G Yan, Z Yang, Y Su… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
If the brain is regarded as a system, it will be one of the most complex systems in the
universe. Traditional analysis and classification methods of major depressive disorder …

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 …

Resting-state EEG signal for major depressive disorder detection: A systematic validation on a large and diverse dataset

CT Wu, HC Huang, S Huang, IM Chen, SC Liao… - Biosensors, 2021 - mdpi.com
Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes
of disability. Machine learning combined with non-invasive electroencephalography (EEG) …

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

Z Chen, B Hu, X Liu, B Becker, SB Eickhoff, K Miao… - BMC medicine, 2023 - Springer
Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …

Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review

Z Chen, X Liu, Q Yang, YJ Wang, K Miao… - JAMA network …, 2023 - jamanetwork.com
Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …

A depression prediction algorithm based on spatiotemporal feature of EEG signal

W Liu, K Jia, Z Wang, Z Ma - Brain Sciences, 2022 - mdpi.com
Depression has gradually become the most common mental disorder in the world. The
accuracy of its diagnosis may be affected by many factors, while the primary diagnosis …

[HTML][HTML] The effect of music listening on EEG functional connectivity of brain: a short-duration and long-duration study

D Mahmood, H Nisar, VV Yap, CY Tsai - Mathematics, 2022 - mdpi.com
Music is considered a powerful brain stimulus, as listening to it can activate several brain
networks. Music of different kinds and genres may have a different effect on the human …

Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review

C Greco, O Matarazzo, G Cordasco, A Vinciarelli… - Ieee …, 2021 - ieeexplore.ieee.org
Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly
based on subjective assessments and self-reported measures. However, objective criteria …