Generative Adversarial Network (GAN) for Simulating Electroencephalography.
Electroencephalography
Generative adversarial networks
Machine learning
Journal
Brain topography
ISSN: 1573-6792
Titre abrégé: Brain Topogr
Pays: United States
ID NLM: 8903034
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
24
11
2022
accepted:
22
06
2023
medline:
14
8
2023
pubmed:
6
7
2023
entrez:
6
7
2023
Statut:
ppublish
Résumé
Electroencephalographs record the electrical activity of your brain through the scalp. Electroencephalography is difficult to obtain due to its sensitivity and variability. Applications of electroencephalography such as for diagnosis, education, brain-computer interfaces require large samples of electroencephalography recording, however, it is often difficult to obtain the required datasets. Generative adversarial networks are robust deep learning framework which have proven themselves to be capable of synthesizing data. The robust nature of a generative adversarial network was used to generate multi-channel electroencephalography data in order to see if generative adversarial networks could reconstruct the spatio-temporal aspects of multi-channel electroencephalography signals. We were able to find that the synthetic electroencephalography data was able to replicate fine details of electroencephalography data and could potentially help us to generate large sample synthetic resting-state electroencephalography data for use in simulation testing of neuroimaging analyses. Generative adversarial networks (GANs) are robust deep-learning frameworks that can be trained to be convincing replicants of real data GANs were capable of generating "fake" EEG data that replicated fine details and topographies of "real" resting-state EEG data.
Identifiants
pubmed: 37410276
doi: 10.1007/s10548-023-00986-5
pii: 10.1007/s10548-023-00986-5
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
661-670Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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