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...

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Autores principales:	Joonas Iivanainen, Antti J. Mäkinen, Rasmus Zetter, Matti Stenroos, Risto J. Ilmoniemi, Lauri Parkkonen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Magnetoencephalography Electroencephalography On-scalp MEG Spatial sampling Optimal design Spatial frequency Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/c76707be7d064028b2f5ef440d8736b5
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id	oai:doaj.org-article:c76707be7d064028b2f5ef440d8736b5
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spelling	oai:doaj.org-article:c76707be7d064028b2f5ef440d8736b52021-12-04T04:33:17ZSpatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design1095-957210.1016/j.neuroimage.2021.118747https://doaj.org/article/c76707be7d064028b2f5ef440d8736b52021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1053811921010193https://doaj.org/toc/1095-9572In 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.Joonas IivanainenAntti J. MäkinenRasmus ZetterMatti StenroosRisto J. IlmoniemiLauri ParkkonenElsevierarticleMagnetoencephalographyElectroencephalographyOn-scalp MEGSpatial samplingOptimal designSpatial frequencyNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroImage, Vol 245, Iss , Pp 118747- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Magnetoencephalography Electroencephalography On-scalp MEG Spatial sampling Optimal design Spatial frequency Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Magnetoencephalography Electroencephalography On-scalp MEG Spatial sampling Optimal design Spatial frequency Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 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
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.
format	article
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
title	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_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_sort	spatial sampling of meg and eeg based on generalized spatial-frequency analysis and optimal design
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/c76707be7d064028b2f5ef440d8736b5
work_keys_str_mv	AT joonasiivanainen spatialsamplingofmegandeegbasedongeneralizedspatialfrequencyanalysisandoptimaldesign AT anttijmakinen spatialsamplingofmegandeegbasedongeneralizedspatialfrequencyanalysisandoptimaldesign AT rasmuszetter spatialsamplingofmegandeegbasedongeneralizedspatialfrequencyanalysisandoptimaldesign AT mattistenroos spatialsamplingofmegandeegbasedongeneralizedspatialfrequencyanalysisandoptimaldesign AT ristojilmoniemi spatialsamplingofmegandeegbasedongeneralizedspatialfrequencyanalysisandoptimaldesign AT lauriparkkonen spatialsamplingofmegandeegbasedongeneralizedspatialfrequencyanalysisandoptimaldesign
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Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design

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