Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks
Name
fnins-15-552666.pdf
Description
Published version
Size
4.92 MB
Format
Adobe PDF
Checksum (MD5)
902d48bbe99ee3ba04b4882f385fff2b
Author(s)
Dinh, Christoph
Samuelsson, John G
Hunold, Alexander
Hämäläinen, Matti S
Khan, Sheraz
Date Issued
2021
Journal
Frontiers in Neuroscience
Publisher
Frontiers Media SA
Version
Final published version
Abstract
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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fnins.2021.552666
