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Forward Field Computation with OpenMEEG
To recover the sources giving rise to electro- and magnetoencephalography in individual measurements, realistic physiological modeling is required, and accurate numerical solutions must be computed. We present OpenMEEG, which solves the electromagnetic forward problem in the quasistatic regime, for...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3061324/ https://www.ncbi.nlm.nih.gov/pubmed/21437231 http://dx.doi.org/10.1155/2011/923703 |
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author | Gramfort, Alexandre Papadopoulo, Théodore Olivi, Emmanuel Clerc, Maureen |
author_facet | Gramfort, Alexandre Papadopoulo, Théodore Olivi, Emmanuel Clerc, Maureen |
author_sort | Gramfort, Alexandre |
collection | PubMed |
description | To recover the sources giving rise to electro- and magnetoencephalography in individual measurements, realistic physiological modeling is required, and accurate numerical solutions must be computed. We present OpenMEEG, which solves the electromagnetic forward problem in the quasistatic regime, for head models with piecewise constant conductivity. The core of OpenMEEG consists of the symmetric Boundary Element Method, which is based on an extended Green Representation theorem. OpenMEEG is able to provide lead fields for four different electromagnetic forward problems: Electroencephalography (EEG), Magnetoencephalography (MEG), Electrical Impedance Tomography (EIT), and intracranial electric potentials (IPs). OpenMEEG is open source and multiplatform. It can be used from Python and Matlab in conjunction with toolboxes that solve the inverse problem; its integration within FieldTrip is operational since release 2.0. |
format | Text |
id | pubmed-3061324 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-30613242011-03-24 Forward Field Computation with OpenMEEG Gramfort, Alexandre Papadopoulo, Théodore Olivi, Emmanuel Clerc, Maureen Comput Intell Neurosci Research Article To recover the sources giving rise to electro- and magnetoencephalography in individual measurements, realistic physiological modeling is required, and accurate numerical solutions must be computed. We present OpenMEEG, which solves the electromagnetic forward problem in the quasistatic regime, for head models with piecewise constant conductivity. The core of OpenMEEG consists of the symmetric Boundary Element Method, which is based on an extended Green Representation theorem. OpenMEEG is able to provide lead fields for four different electromagnetic forward problems: Electroencephalography (EEG), Magnetoencephalography (MEG), Electrical Impedance Tomography (EIT), and intracranial electric potentials (IPs). OpenMEEG is open source and multiplatform. It can be used from Python and Matlab in conjunction with toolboxes that solve the inverse problem; its integration within FieldTrip is operational since release 2.0. Hindawi Publishing Corporation 2011 2011-03-14 /pmc/articles/PMC3061324/ /pubmed/21437231 http://dx.doi.org/10.1155/2011/923703 Text en Copyright © 2011 Alexandre Gramfort et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Gramfort, Alexandre Papadopoulo, Théodore Olivi, Emmanuel Clerc, Maureen Forward Field Computation with OpenMEEG |
title | Forward Field Computation with OpenMEEG |
title_full | Forward Field Computation with OpenMEEG |
title_fullStr | Forward Field Computation with OpenMEEG |
title_full_unstemmed | Forward Field Computation with OpenMEEG |
title_short | Forward Field Computation with OpenMEEG |
title_sort | forward field computation with openmeeg |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3061324/ https://www.ncbi.nlm.nih.gov/pubmed/21437231 http://dx.doi.org/10.1155/2011/923703 |
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