Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design

Joonas Iivanainen; Antti J. Mäkinen; Rasmus Zetter; Matti Stenroos; Risto J. Ilmoniemi; Lauri Parkkonen

doi:10.1016/j.neuroimage.2021.118747

Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design

Joonas Iivanainen^*, Antti J. Mäkinen, Rasmus Zetter, Matti Stenroos, Risto J. Ilmoniemi, Lauri Parkkonen

^*Tämän työn vastaava kirjoittaja

Neurotieteen ja lääketieteellisen tekniikan laitos

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

24 Sitaatiot (Scopus)

148 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Artikkeli	118747
Sivut	1-15
Sivumäärä	15
Julkaisu	NeuroImage
Vuosikerta	245
DOI - pysyväislinkit	https://doi.org/10.1016/j.neuroimage.2021.118747
Tila	Julkaistu - 15 jouluk. 2021
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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10.1016/j.neuroimage.2021.118747Lisenssi: CC BY-NC-ND

Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal designFinal published version, 4,24 MBLisenssi: CC BY-NC-ND

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Linkki julkaisuun Scopuksessa

HRMEG: High-resolution magnetoencephalography: Towards non-invasive corticography
Iivanainen, J. (Projektin jäsen), Parkkonen, L. (Vastuullinen tutkija), Zubarev, I. (Projektin jäsen), Anelli, M. (Projektin jäsen), Avendano, J. (Projektin jäsen), Grön, M. (Projektin jäsen), Helle, L. (Projektin jäsen), Nurminen, M. (Projektin jäsen), Zetter, R. (Projektin jäsen), Hietala, P. (Projektin jäsen), Yamin, A. (Projektin jäsen), Henttonen, M. (Projektin jäsen), Puthanmadam Subramaniyam, N. (Projektin jäsen), Zhigalov, A. (Projektin jäsen), Ahola, O. (Projektin jäsen), Simanainen, S. (Projektin jäsen), Pfeiffer, C. (Projektin jäsen), Lauronen, S. (Projektin jäsen) & Terborg, H. (Projektin jäsen)
22/08/2016 → 31/08/2022
Projekti: EU: ERC grants

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title = "Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design",

abstract = "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.",

keywords = "Electroencephalography, Magnetoencephalography, On-scalp MEG, Optimal design, Spatial frequency, Spatial sampling",

author = "Joonas Iivanainen and M{\"a}kinen, {Antti J.} and Rasmus Zetter and Matti Stenroos and Ilmoniemi, {Risto J.} and Lauri Parkkonen",

note = "| openaire: EC/H2020/686865/EU//BREAKBEN | openaire: EC/H2020/678578/EU//HRMEG Funding Information: This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 686865 (project BREAKBEN), the European Research Council under grant agreement No 678578 (project HRMEG), the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number R01NS094604, the Finnish Cultural Foundation under grant Nos. 00170330 and 00180388 (JI), and Vilho, Yrj{\"o} and Kalle V{\"a}is{\"a}l{\"a} Foundation (AM). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations. Publisher Copyright: {\textcopyright} 2021",

year = "2021",

month = dec,

day = "15",

doi = "10.1016/j.neuroimage.2021.118747",

language = "English",

volume = "245",

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journal = "NeuroImage",

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T1 - Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design

AU - Iivanainen, Joonas

AU - Mäkinen, Antti J.

AU - Zetter, Rasmus

AU - Stenroos, Matti

AU - Ilmoniemi, Risto J.

AU - Parkkonen, Lauri

N1 - | openaire: EC/H2020/686865/EU//BREAKBEN | openaire: EC/H2020/678578/EU//HRMEG Funding Information: This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 686865 (project BREAKBEN), the European Research Council under grant agreement No 678578 (project HRMEG), the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number R01NS094604, the Finnish Cultural Foundation under grant Nos. 00170330 and 00180388 (JI), and Vilho, Yrjö and Kalle Väisälä Foundation (AM). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations. Publisher Copyright: © 2021

PY - 2021/12/15

Y1 - 2021/12/15

N2 - 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.

AB - 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.

KW - Electroencephalography

KW - Magnetoencephalography

KW - On-scalp MEG

KW - Optimal design

KW - Spatial frequency

KW - Spatial sampling

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U2 - 10.1016/j.neuroimage.2021.118747

DO - 10.1016/j.neuroimage.2021.118747

M3 - Article

C2 - 34852277

AN - SCOPUS:85120450312

SN - 1053-8119

VL - 245

SP - 1

EP - 15

JO - NeuroImage

JF - NeuroImage

M1 - 118747

ER -

Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design

Abstrakti

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

HRMEG: High-resolution magnetoencephalography: Towards non-invasive corticography

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