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

^*Corresponding author for this work

Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020
Editors	Tomaz Jarm, Aleksandra Cvetkoska, Samo Mahnič-Kalamiza, Damijan Miklavcic
Publisher	Springer
Pages	1019-1027
Number of pages	9
ISBN (Print)	9783030646097
DOIs	https://doi.org/10.1007/978-3-030-64610-3_114
Publication status	Published - 2021
Publication type	A4 Article in conference proceedings
Event	European Medical and Biological Engineering Conference - Portorož, Slovenia Duration: 29 Nov 2020 → 3 Dec 2020

Publication series

Name	IFMBE Proceedings
Volume	80
ISSN (Print)	1680-0737
ISSN (Electronic)	1433-9277

Conference

Conference	European Medical and Biological Engineering Conference
Country/Territory	Slovenia
City	Portorož
Period	29/11/20 → 3/12/20

Funding

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).

Keywords

Bayesian modelling
Electroencephalography
Inverse problem
Skull conductivity
Source imaging

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Bioengineering
Biomedical Engineering

Access to Document

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

Cite this

Koulouri, A., & Rimpiläinen, V. (2021). Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling. In T. Jarm, A. Cvetkoska, S. Mahnič-Kalamiza, & D. Miklavcic (Eds.), 8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020 (pp. 1019-1027). (IFMBE Proceedings; Vol. 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. editor / Tomaz Jarm ; Aleksandra Cvetkoska ; Samo Mahnič-Kalamiza ; Damijan Miklavcic. Springer, 2021. pp. 1019-1027 (IFMBE Proceedings).

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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",

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Koulouri, A & Rimpiläinen, V 2021, Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling. in T Jarm, A Cvetkoska, S Mahnič-Kalamiza & D Miklavcic (eds), 8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020. IFMBE Proceedings, vol. 80, Springer, pp. 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. ed. / Tomaz Jarm; Aleksandra Cvetkoska; Samo Mahnič-Kalamiza; Damijan Miklavcic. Springer, 2021. p. 1019-1027 (IFMBE Proceedings; Vol. 80).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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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

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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.

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KW - Electroencephalography

KW - Inverse problem

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KW - Source imaging

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PB - Springer

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