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Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG
Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electro...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7783406/ https://www.ncbi.nlm.nih.gov/pubmed/33414699 http://dx.doi.org/10.3389/fnins.2020.558981 |
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author | Bhutada, Abhishek S. Sepúlveda, Pradyumna Torres, Rafael Ossandón, Tomás Ruiz, Sergio Sitaram, Ranganatha |
author_facet | Bhutada, Abhishek S. Sepúlveda, Pradyumna Torres, Rafael Ossandón, Tomás Ruiz, Sergio Sitaram, Ranganatha |
author_sort | Bhutada, Abhishek S. |
collection | PubMed |
description | Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode’s distance profile in relation to other electrodes. We then compare the subject’s electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects’ data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup. |
format | Online Article Text |
id | pubmed-7783406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77834062021-01-06 Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG Bhutada, Abhishek S. Sepúlveda, Pradyumna Torres, Rafael Ossandón, Tomás Ruiz, Sergio Sitaram, Ranganatha Front Neurosci Neuroscience Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode’s distance profile in relation to other electrodes. We then compare the subject’s electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects’ data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup. Frontiers Media S.A. 2020-12-22 /pmc/articles/PMC7783406/ /pubmed/33414699 http://dx.doi.org/10.3389/fnins.2020.558981 Text en Copyright © 2020 Bhutada, Sepúlveda, Torres, Ossandón, Ruiz and Sitaram. http://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 | Neuroscience Bhutada, Abhishek S. Sepúlveda, Pradyumna Torres, Rafael Ossandón, Tomás Ruiz, Sergio Sitaram, Ranganatha Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG |
title | Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG |
title_full | Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG |
title_fullStr | Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG |
title_full_unstemmed | Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG |
title_short | Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG |
title_sort | semi-automated and direct localization and labeling of eeg electrodes using mr structural images for simultaneous fmri-eeg |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7783406/ https://www.ncbi.nlm.nih.gov/pubmed/33414699 http://dx.doi.org/10.3389/fnins.2020.558981 |
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