Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

IntroductionMajor depressive disorder (MDD) is the most common mental disorder worldwide, leading to impairment in quality and independence of life. Electroencephalography (EEG) biomarkers processed with machine learning (ML) algorithms have been explored for objective diagnoses with promising resul...

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Bibliographic Details
Main Authors: Friedrich Philipp Carrle, Yasmin Hollenbenders, Alexandra Reichenbach
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-10-01
Series:Frontiers in Neuroscience
Subjects:
major depressive disorder
electroencephalography
generative adversarial network
deep learning
data augmentation
synthetic data
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2023.1219133/full

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