MEG Source Localization via Deep Learning.
deep learning
inverse problems
magnetoencephalography
source localization
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
22 Jun 2021
22 Jun 2021
Historique:
received:
29
04
2021
revised:
14
06
2021
accepted:
17
06
2021
entrez:
2
7
2021
pubmed:
3
7
2021
medline:
7
7
2021
Statut:
epublish
Résumé
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to single and multiple time point MEG data, and can estimate varying numbers of dipole sources. Results from simulated MEG data on the cortical surface of a real human subject demonstrated improvements against the popular RAP-MUSIC localization algorithm in specific scenarios with varying SNR levels, inter-source correlation values, and number of sources. Importantly, the deep learning models had robust performance to forward model errors resulting from head translation and rotation and a significant reduction in computation time, to a fraction of 1 ms, paving the way to real-time MEG source localization.
Identifiants
pubmed: 34206620
pii: s21134278
doi: 10.3390/s21134278
pmc: PMC8271934
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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