Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design
In this paper, we analyze spatial sampling of electro- (EEG) and magnetoencephalography (MEG), where the electric or magnetic field is typically sampled on a curved surface such as the scalp. By simulating fields originating from a representative adult-male head, we study the spatial-frequency conte...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921010193 |
| _version_ | 1819096997494259712 |
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| author | Joonas Iivanainen Antti J. Mäkinen Rasmus Zetter Matti Stenroos Risto J. Ilmoniemi Lauri Parkkonen |
| author_facet | Joonas Iivanainen Antti J. Mäkinen Rasmus Zetter Matti Stenroos Risto J. Ilmoniemi Lauri Parkkonen |
| author_sort | Joonas Iivanainen |
| collection | DOAJ |
| description | In this paper, we analyze spatial sampling of electro- (EEG) and magnetoencephalography (MEG), where the electric or magnetic field is typically sampled on a curved surface such as the scalp. By simulating fields originating from a representative adult-male head, we study the spatial-frequency content in EEG as well as in on- and off-scalp MEG. This analysis suggests that on-scalp MEG, off-scalp MEG and EEG can benefit from up to 280, 90 and 110 spatial samples, respectively. In addition, we suggest a new approach to obtain sensor locations that are optimal with respect to prior assumptions. The approach also allows to control, e.g., the uniformity of the sensor locations. Based on our simulations, we argue that for a low number of spatial samples, model-informed non-uniform sampling can be beneficial. For a large number of samples, uniform sampling grids yield nearly the same total information as the model-informed grids. |
| first_indexed | 2024-12-22T00:08:05Z |
| format | Article |
| id | doaj.art-c76707be7d064028b2f5ef440d8736b5 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-22T00:08:05Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-c76707be7d064028b2f5ef440d8736b52022-12-21T18:45:31ZengElsevierNeuroImage1095-95722021-12-01245118747Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal designJoonas Iivanainen0Antti J. Mäkinen1Rasmus Zetter2Matti Stenroos3Risto J. Ilmoniemi4Lauri Parkkonen5Corresponding author.; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto FI-00076, FinlandDepartment of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto FI-00076, FinlandDepartment of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto FI-00076, FinlandDepartment of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto FI-00076, FinlandDepartment of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto FI-00076, FinlandDepartment of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto FI-00076, FinlandIn this paper, we analyze spatial sampling of electro- (EEG) and magnetoencephalography (MEG), where the electric or magnetic field is typically sampled on a curved surface such as the scalp. By simulating fields originating from a representative adult-male head, we study the spatial-frequency content in EEG as well as in on- and off-scalp MEG. This analysis suggests that on-scalp MEG, off-scalp MEG and EEG can benefit from up to 280, 90 and 110 spatial samples, respectively. In addition, we suggest a new approach to obtain sensor locations that are optimal with respect to prior assumptions. The approach also allows to control, e.g., the uniformity of the sensor locations. Based on our simulations, we argue that for a low number of spatial samples, model-informed non-uniform sampling can be beneficial. For a large number of samples, uniform sampling grids yield nearly the same total information as the model-informed grids.http://www.sciencedirect.com/science/article/pii/S1053811921010193MagnetoencephalographyElectroencephalographyOn-scalp MEGSpatial samplingOptimal designSpatial frequency |
| spellingShingle | Joonas Iivanainen Antti J. Mäkinen Rasmus Zetter Matti Stenroos Risto J. Ilmoniemi Lauri Parkkonen Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design NeuroImage Magnetoencephalography Electroencephalography On-scalp MEG Spatial sampling Optimal design Spatial frequency |
| title | Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design |
| title_full | Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design |
| title_fullStr | Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design |
| title_full_unstemmed | Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design |
| title_short | Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design |
| title_sort | spatial sampling of meg and eeg based on generalized spatial frequency analysis and optimal design |
| topic | Magnetoencephalography Electroencephalography On-scalp MEG Spatial sampling Optimal design Spatial frequency |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921010193 |
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