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Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions
The simultaneous acquisition of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) aims to measure brain activity with good spatial and temporal resolution. This bimodal neuroimaging can bring complementary and very relevant information in many cases and in particu...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8296904/ https://www.ncbi.nlm.nih.gov/pubmed/34305777 http://dx.doi.org/10.3389/fneur.2021.644278 |
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author | Pinte, Caroline Fleury, Mathis Maurel, Pierre |
author_facet | Pinte, Caroline Fleury, Mathis Maurel, Pierre |
author_sort | Pinte, Caroline |
collection | PubMed |
description | The simultaneous acquisition of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) aims to measure brain activity with good spatial and temporal resolution. This bimodal neuroimaging can bring complementary and very relevant information in many cases and in particular for epilepsy. Indeed, it has been shown that it can facilitate the localization of epileptic networks. Regarding the EEG, source localization requires the resolution of a complex inverse problem that depends on several parameters, one of the most important of which is the position of the EEG electrodes on the scalp. These positions are often roughly estimated using fiducial points. In simultaneous EEG-fMRI acquisitions, specific MRI sequences can provide valuable spatial information. In this work, we propose a new fully automatic method based on neural networks to segment an ultra-short echo-time MR volume in order to retrieve the coordinates and labels of the EEG electrodes. It consists of two steps: a segmentation of the images by a neural network, followed by the registration of an EEG template on the obtained detections. We trained the neural network using 37 MR volumes and then we tested our method on 23 new volumes. The results show an average detection accuracy of 99.7% with an average position error of 2.24 mm, as well as 100% accuracy in the labeling. |
format | Online Article Text |
id | pubmed-8296904 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82969042021-07-23 Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions Pinte, Caroline Fleury, Mathis Maurel, Pierre Front Neurol Neurology The simultaneous acquisition of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) aims to measure brain activity with good spatial and temporal resolution. This bimodal neuroimaging can bring complementary and very relevant information in many cases and in particular for epilepsy. Indeed, it has been shown that it can facilitate the localization of epileptic networks. Regarding the EEG, source localization requires the resolution of a complex inverse problem that depends on several parameters, one of the most important of which is the position of the EEG electrodes on the scalp. These positions are often roughly estimated using fiducial points. In simultaneous EEG-fMRI acquisitions, specific MRI sequences can provide valuable spatial information. In this work, we propose a new fully automatic method based on neural networks to segment an ultra-short echo-time MR volume in order to retrieve the coordinates and labels of the EEG electrodes. It consists of two steps: a segmentation of the images by a neural network, followed by the registration of an EEG template on the obtained detections. We trained the neural network using 37 MR volumes and then we tested our method on 23 new volumes. The results show an average detection accuracy of 99.7% with an average position error of 2.24 mm, as well as 100% accuracy in the labeling. Frontiers Media S.A. 2021-07-08 /pmc/articles/PMC8296904/ /pubmed/34305777 http://dx.doi.org/10.3389/fneur.2021.644278 Text en Copyright © 2021 Pinte, Fleury and Maurel. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Pinte, Caroline Fleury, Mathis Maurel, Pierre Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions |
title | Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions |
title_full | Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions |
title_fullStr | Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions |
title_full_unstemmed | Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions |
title_short | Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions |
title_sort | deep learning-based localization of eeg electrodes within mri acquisitions |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8296904/ https://www.ncbi.nlm.nih.gov/pubmed/34305777 http://dx.doi.org/10.3389/fneur.2021.644278 |
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