Optimization of OPM-MEG Layouts with a Limited Number of Sensors
Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that measures weak magnetic fields generated by neural electrical activity in the brain. Traditional MEG systems use superconducting quantum interference device (SQUID) sensors, which require cryogenic cooling and employ a dense a...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2706 |
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| author | Urban Marhl Rok Hren Tilmann Sander Vojko Jazbinšek |
| author_facet | Urban Marhl Rok Hren Tilmann Sander Vojko Jazbinšek |
| author_sort | Urban Marhl |
| collection | DOAJ |
| description | Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that measures weak magnetic fields generated by neural electrical activity in the brain. Traditional MEG systems use superconducting quantum interference device (SQUID) sensors, which require cryogenic cooling and employ a dense array of sensors to capture magnetic field maps (MFMs) around the head. Recent advancements have introduced optically pumped magnetometers (OPMs) as a promising alternative. Unlike SQUIDs, OPMs do not require cooling and can be placed closer to regions of interest (ROIs). This study aims to optimize the layout of OPM-MEG sensors, maximizing information capture with a limited number of sensors. We applied a sequential selection algorithm (SSA), originally developed for body surface potential mapping in electrocardiography, which requires a large database of full-head MFMs. While modern OPM-MEG systems offer full-head coverage, expected future clinical use will benefit from simplified procedures, where handling a lower number of sensors is easier and more efficient. To explore this, we converted full-head SQUID-MEG measurements of auditory-evoked fields (AEFs) into OPM-MEG layouts with 80 sensor sites. System conversion was done by calculating a current distribution on the brain surface using minimum norm estimation (MNE). We evaluated the SSA’s performance under different protocols, for example, using measurements of single or combined OPM components. We assessed the quality of estimated MFMs using metrics, such as the correlation coefficient (CC), root-mean-square error, and relative error. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95, localization error < 1 mm) capture most of the information contained in full-head MFMs. Our main finding is that for event-related fields, such as AEFs, which primarily originate from focal sources, a significantly smaller number of sensors than currently used in conventional MEG systems is sufficient to extract relevant information. |
| format | Article |
| id | doaj-art-c78f1cb378f54b57adcf9f3b5cf5de4f |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c78f1cb378f54b57adcf9f3b5cf5de4f2025-08-20T01:49:11ZengMDPI AGSensors1424-82202025-04-01259270610.3390/s25092706Optimization of OPM-MEG Layouts with a Limited Number of SensorsUrban Marhl0Rok Hren1Tilmann Sander2Vojko Jazbinšek3Institute of Mathematics, Physics and Mechanics, SI-1000 Ljubljana, SloveniaInstitute of Mathematics, Physics and Mechanics, SI-1000 Ljubljana, SloveniaPhysikalisch-Technische Bundesanstalt, 10587 Berlin, GermanyInstitute of Mathematics, Physics and Mechanics, SI-1000 Ljubljana, SloveniaMagnetoencephalography (MEG) is a non-invasive neuroimaging technique that measures weak magnetic fields generated by neural electrical activity in the brain. Traditional MEG systems use superconducting quantum interference device (SQUID) sensors, which require cryogenic cooling and employ a dense array of sensors to capture magnetic field maps (MFMs) around the head. Recent advancements have introduced optically pumped magnetometers (OPMs) as a promising alternative. Unlike SQUIDs, OPMs do not require cooling and can be placed closer to regions of interest (ROIs). This study aims to optimize the layout of OPM-MEG sensors, maximizing information capture with a limited number of sensors. We applied a sequential selection algorithm (SSA), originally developed for body surface potential mapping in electrocardiography, which requires a large database of full-head MFMs. While modern OPM-MEG systems offer full-head coverage, expected future clinical use will benefit from simplified procedures, where handling a lower number of sensors is easier and more efficient. To explore this, we converted full-head SQUID-MEG measurements of auditory-evoked fields (AEFs) into OPM-MEG layouts with 80 sensor sites. System conversion was done by calculating a current distribution on the brain surface using minimum norm estimation (MNE). We evaluated the SSA’s performance under different protocols, for example, using measurements of single or combined OPM components. We assessed the quality of estimated MFMs using metrics, such as the correlation coefficient (CC), root-mean-square error, and relative error. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95, localization error < 1 mm) capture most of the information contained in full-head MFMs. Our main finding is that for event-related fields, such as AEFs, which primarily originate from focal sources, a significantly smaller number of sensors than currently used in conventional MEG systems is sufficient to extract relevant information.https://www.mdpi.com/1424-8220/25/9/2706magnetoencephalographyoptically pumped magnetometerssensor optimizationsequential selection algorithmauditory-evoked fieldsminimum norm estimation |
| spellingShingle | Urban Marhl Rok Hren Tilmann Sander Vojko Jazbinšek Optimization of OPM-MEG Layouts with a Limited Number of Sensors Sensors magnetoencephalography optically pumped magnetometers sensor optimization sequential selection algorithm auditory-evoked fields minimum norm estimation |
| title | Optimization of OPM-MEG Layouts with a Limited Number of Sensors |
| title_full | Optimization of OPM-MEG Layouts with a Limited Number of Sensors |
| title_fullStr | Optimization of OPM-MEG Layouts with a Limited Number of Sensors |
| title_full_unstemmed | Optimization of OPM-MEG Layouts with a Limited Number of Sensors |
| title_short | Optimization of OPM-MEG Layouts with a Limited Number of Sensors |
| title_sort | optimization of opm meg layouts with a limited number of sensors |
| topic | magnetoencephalography optically pumped magnetometers sensor optimization sequential selection algorithm auditory-evoked fields minimum norm estimation |
| url | https://www.mdpi.com/1424-8220/25/9/2706 |
| work_keys_str_mv | AT urbanmarhl optimizationofopmmeglayoutswithalimitednumberofsensors AT rokhren optimizationofopmmeglayoutswithalimitednumberofsensors AT tilmannsander optimizationofopmmeglayoutswithalimitednumberofsensors AT vojkojazbinsek optimizationofopmmeglayoutswithalimitednumberofsensors |