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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...

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Autores principales: Bhutada, Abhishek S., Sepúlveda, Pradyumna, Torres, Rafael, Ossandón, Tomás, Ruiz, Sergio, Sitaram, Ranganatha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
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.
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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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