Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling

Alexandra Koulouri; Ville Rimpiläinen

doi:10.1007/978-3-030-64610-3_114

Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling

Alexandra Koulouri^*
, Ville Rimpiläinen

^*Tämän työn vastaava kirjoittaja

Tietotekniikka

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

2 Sitaatiot (Scopus)

Abstrakti

The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conductivity parameter. In this paper, we propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors due to the unknown skull conductivity and, simultaneously, to compute a low-order estimate for the actual skull conductivity value. By using simulated EEG data that corresponds to focal source activity, we demonstrate the potential of the method to reconstruct the underlying focal sources and low-order errors induced by the unknown skull conductivity. Subsequently, the estimated errors are used to approximate the skull conductivity. The results indicate clear improvements in the source localization accuracy and feasible skull conductivity estimates.

Alkuperäiskieli	Englanti
Otsikko	8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020
Toimittajat	Tomaz Jarm, Aleksandra Cvetkoska, Samo Mahnič-Kalamiza, Damijan Miklavcic
Kustantaja	Springer
Sivut	1019-1027
Sivumäärä	9
ISBN (painettu)	9783030646097
DOI - pysyväislinkit	https://doi.org/10.1007/978-3-030-64610-3_114
Tila	Julkaistu - 2021
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	European Medical and Biological Engineering Conference - Portorož, Slovenia Kesto: 29 marrask. 2020 → 3 jouluk. 2020

Julkaisusarja

Nimi	IFMBE Proceedings
Vuosikerta	80
ISSN (painettu)	1680-0737
ISSN (elektroninen)	1433-9277

Conference

Conference	European Medical and Biological Engineering Conference
Maa/Alue	Slovenia
Kaupunki	Portorož
Ajanjakso	29/11/20 → 3/12/20

Rahoitus

Acknowledgements. This project has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222 and from the Academy of Finland post-doctoral program (project no. 316542).

Julkaisufoorumi-taso

Jufo-taso 1

!!ASJC Scopus subject areas

Bioengineering
Biomedical Engineering

Pääsy asiakirjaan

10.1007/978-3-030-64610-3_114

Siteeraa tätä

Koulouri, A., & Rimpiläinen, V. (2021). Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling. teoksessa T. Jarm, A. Cvetkoska, S. Mahnič-Kalamiza, & D. Miklavcic (Toimittajat), 8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020 (Sivut 1019-1027). (IFMBE Proceedings; Vuosikerta 80). Springer. https://doi.org/10.1007/978-3-030-64610-3_114

Koulouri, Alexandra ; Rimpiläinen, Ville. / Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling. 8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020. Toimittaja / Tomaz Jarm ; Aleksandra Cvetkoska ; Samo Mahnič-Kalamiza ; Damijan Miklavcic. Springer, 2021. Sivut 1019-1027 (IFMBE Proceedings).

@inproceedings{1835662baf0b48ac8ce25c891d87281a,

title = "Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling",

abstract = "The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conductivity parameter. In this paper, we propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors due to the unknown skull conductivity and, simultaneously, to compute a low-order estimate for the actual skull conductivity value. By using simulated EEG data that corresponds to focal source activity, we demonstrate the potential of the method to reconstruct the underlying focal sources and low-order errors induced by the unknown skull conductivity. Subsequently, the estimated errors are used to approximate the skull conductivity. The results indicate clear improvements in the source localization accuracy and feasible skull conductivity estimates.",

keywords = "Bayesian modelling, Electroencephalography, Inverse problem, Skull conductivity, Source imaging",

author = "Alexandra Koulouri and Ville Rimpil{\"a}inen",

note = "Funding Information: Acknowledgements. This project has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222 and from the Academy of Finland post-doctoral program (project no. 316542). Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved. jufoid=58152; European Medical and Biological Engineering Conference ; Conference date: 29-11-2020 Through 03-12-2020",

year = "2021",

doi = "10.1007/978-3-030-64610-3\_114",

language = "English",

isbn = "9783030646097",

series = "IFMBE Proceedings",

publisher = "Springer",

pages = "1019--1027",

editor = "Tomaz Jarm and Aleksandra Cvetkoska and Samo Mahni{\v c}-Kalamiza and Damijan Miklavcic",

booktitle = "8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020",

}

Koulouri, A & Rimpiläinen, V 2021, Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling. julkaisussa T Jarm, A Cvetkoska, S Mahnič-Kalamiza & D Miklavcic (toim), 8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020. IFMBE Proceedings, Vuosikerta 80, Springer, Sivut 1019-1027, European Medical and Biological Engineering Conference, Portorož, Slovenia, 29/11/20. https://doi.org/10.1007/978-3-030-64610-3_114

Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling. / Koulouri, Alexandra; Rimpiläinen, Ville.
8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020. toim. / Tomaz Jarm; Aleksandra Cvetkoska; Samo Mahnič-Kalamiza; Damijan Miklavcic. Springer, 2021. s. 1019-1027 (IFMBE Proceedings; Vuosikerta 80).

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

T1 - Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling

AU - Koulouri, Alexandra

AU - Rimpiläinen, Ville

N1 - Funding Information: Acknowledgements. This project has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222 and from the Academy of Finland post-doctoral program (project no. 316542). Publisher Copyright: © 2021, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved. jufoid=58152

PY - 2021

Y1 - 2021

N2 - The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conductivity parameter. In this paper, we propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors due to the unknown skull conductivity and, simultaneously, to compute a low-order estimate for the actual skull conductivity value. By using simulated EEG data that corresponds to focal source activity, we demonstrate the potential of the method to reconstruct the underlying focal sources and low-order errors induced by the unknown skull conductivity. Subsequently, the estimated errors are used to approximate the skull conductivity. The results indicate clear improvements in the source localization accuracy and feasible skull conductivity estimates.

AB - The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conductivity parameter. In this paper, we propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors due to the unknown skull conductivity and, simultaneously, to compute a low-order estimate for the actual skull conductivity value. By using simulated EEG data that corresponds to focal source activity, we demonstrate the potential of the method to reconstruct the underlying focal sources and low-order errors induced by the unknown skull conductivity. Subsequently, the estimated errors are used to approximate the skull conductivity. The results indicate clear improvements in the source localization accuracy and feasible skull conductivity estimates.

KW - Bayesian modelling

KW - Electroencephalography

KW - Inverse problem

KW - Skull conductivity

KW - Source imaging

U2 - 10.1007/978-3-030-64610-3_114

DO - 10.1007/978-3-030-64610-3_114

M3 - Conference contribution

AN - SCOPUS:85097628437

SN - 9783030646097

T3 - IFMBE Proceedings

SP - 1019

EP - 1027

BT - 8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020

A2 - Jarm, Tomaz

A2 - Cvetkoska, Aleksandra

A2 - Mahnič-Kalamiza, Samo

A2 - Miklavcic, Damijan

PB - Springer

T2 - European Medical and Biological Engineering Conference

Y2 - 29 November 2020 through 3 December 2020

ER -

Koulouri A, Rimpiläinen V. Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling. julkaisussa Jarm T, Cvetkoska A, Mahnič-Kalamiza S, Miklavcic D, toimittajat, 8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020. Springer. 2021. s. 1019-1027. (IFMBE Proceedings). Epub 2020. doi: 10.1007/978-3-030-64610-3_114