Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset

Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset

IntroductionBrain age prediction has been shown to be clinically relevant, with errors in its prediction associated with various psychiatric and neurological conditions. While the prediction from structural and functional magnetic resonance imaging data has been feasible with high accuracy, whether...

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Bibliographic Details
Main Authors: Mariam Khayretdinova, Alexey Shovkun, Vladislav Degtyarev, Andrey Kiryasov, Polina Pshonkovskaya, Ilya Zakharov
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-12-01
Series:Frontiers in Aging Neuroscience
Subjects:
human brain
brain aging
resting-state EEG
deep learning
relative loss
Online Access:https://www.frontiersin.org/articles/10.3389/fnagi.2022.1019869/full

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https://www.frontiersin.org/articles/10.3389/fnagi.2022.1019869/full

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