ArticleFrontiers in neuroscience2021
Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Deep learning-based EEG source imaging is robust under varying electrode configurations.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Seizure Sources Can Be Imaged from Scalp EEG by Means of Biophysically Constrained Deep Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG.NeuroImage · 2024Article
- Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.NeuroImage · 2023Article
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Authors and funding
5 authors.
Funding
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.
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