Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks.
EEG
LSTM
MEG
deep learning
grid-based Markov localization
source estimation
spatial filtering
spatiotemporal source estimation
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2021
2021
Historique:
received:
16
04
2020
accepted:
25
01
2021
entrez:
26
3
2021
pubmed:
27
3
2021
medline:
27
3
2021
Statut:
epublish
Résumé
Most magneto- and electroencephalography (M/EEG) based source estimation techniques derive their estimates sample wise, independently across time. However, neuronal assemblies are intricately interconnected, constraining the temporal evolution of neural activity that is detected by MEG and EEG; the observed neural currents must thus be highly context dependent. Here, we use a network of Long Short-Term Memory (LSTM) cells where the input is a sequence of past source estimates and the output is a prediction of the following estimate. This prediction is then used to correct the estimate. In this study, we applied this technique on noise-normalized minimum norm estimates (MNE). Because the correction is found by using past activity (context), we call this implementation Contextual MNE (CMNE), although this technique can be used in conjunction with any source estimation method. We test CMNE on simulated epileptiform activity and recorded auditory steady state response (ASSR) data, showing that the CMNE estimates exhibit a higher degree of spatial fidelity than the unfiltered estimates in the tested cases.
Identifiants
pubmed: 33767606
doi: 10.3389/fnins.2021.552666
pmc: PMC7985163
doi:
Types de publication
Journal Article
Langues
eng
Pagination
552666Subventions
Organisme : NIBIB NIH HHS
ID : P41 EB030006
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS104585
Pays : United States
Organisme : NIBIB NIH HHS
ID : T32 EB001680
Pays : United States
Organisme : NIBIB NIH HHS
ID : U01 EB023820
Pays : United States
Informations de copyright
Copyright © 2021 Dinh, Samuelsson, Hunold, Hämäläinen and Khan.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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