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Optimising the sensing volume of OPM sensors for MEG source reconstruction
Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) has been hailed as the future of electrophysiological recordings from the human brain. In this work, we investigate how the dimensions of the sensing volume (the vapour cell) affect the performance of both a single OPM-MEG s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615061/ https://www.ncbi.nlm.nih.gov/pubmed/36403733 http://dx.doi.org/10.1016/j.neuroimage.2022.119747 |
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author | Bezsudnova, Yulia Kowalczyk, Anna U. Koponen, Lari M. Barontini, Giovanni Jensen, Ole |
author_facet | Bezsudnova, Yulia Kowalczyk, Anna U. Koponen, Lari M. Barontini, Giovanni Jensen, Ole |
author_sort | Bezsudnova, Yulia |
collection | PubMed |
description | Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) has been hailed as the future of electrophysiological recordings from the human brain. In this work, we investigate how the dimensions of the sensing volume (the vapour cell) affect the performance of both a single OPM-MEG sensor and a multi-sensor OPM-MEG system. We consider a realistic noise model that accounts for background brain activity and residual noise. By using source reconstruction metrics such as localization accuracy and time-course reconstruction accuracy, we demonstrate that the best overall sensitivity and reconstruction accuracy are achieved with cells that are significantly longer and wider that those of the majority of current commercial OPM sensors. Our work provides useful tools to optimise the cell dimensions of OPM sensors in a wide range of environments. |
format | Online Article Text |
id | pubmed-7615061 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-76150612023-09-08 Optimising the sensing volume of OPM sensors for MEG source reconstruction Bezsudnova, Yulia Kowalczyk, Anna U. Koponen, Lari M. Barontini, Giovanni Jensen, Ole Neuroimage Article Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) has been hailed as the future of electrophysiological recordings from the human brain. In this work, we investigate how the dimensions of the sensing volume (the vapour cell) affect the performance of both a single OPM-MEG sensor and a multi-sensor OPM-MEG system. We consider a realistic noise model that accounts for background brain activity and residual noise. By using source reconstruction metrics such as localization accuracy and time-course reconstruction accuracy, we demonstrate that the best overall sensitivity and reconstruction accuracy are achieved with cells that are significantly longer and wider that those of the majority of current commercial OPM sensors. Our work provides useful tools to optimise the cell dimensions of OPM sensors in a wide range of environments. 2022-12-01 2022-11-18 /pmc/articles/PMC7615061/ /pubmed/36403733 http://dx.doi.org/10.1016/j.neuroimage.2022.119747 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a BY 4.0 (https://creativecommons.org/licenses/by/4.0/) International license. |
spellingShingle | Article Bezsudnova, Yulia Kowalczyk, Anna U. Koponen, Lari M. Barontini, Giovanni Jensen, Ole Optimising the sensing volume of OPM sensors for MEG source reconstruction |
title | Optimising the sensing volume of OPM sensors for MEG source reconstruction |
title_full | Optimising the sensing volume of OPM sensors for MEG source reconstruction |
title_fullStr | Optimising the sensing volume of OPM sensors for MEG source reconstruction |
title_full_unstemmed | Optimising the sensing volume of OPM sensors for MEG source reconstruction |
title_short | Optimising the sensing volume of OPM sensors for MEG source reconstruction |
title_sort | optimising the sensing volume of opm sensors for meg source reconstruction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615061/ https://www.ncbi.nlm.nih.gov/pubmed/36403733 http://dx.doi.org/10.1016/j.neuroimage.2022.119747 |
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