Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE)

Carsten Stahlhut; Morten Mørup; Ole Winther; Lars Kai Hansen

doi:10.1109/MLSP.2009.5306189

Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE)

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

2626 Downloads (Orbit)

Abstract

In this paper we propose an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model is motivated by the many uncertain contributions that form the forward propagation model including the tissue conductivity distribution, the cortical surface, and electrode positions. We first present a hierarchical Bayesian framework for EEG source localization that jointly performs source and forward model reconstruction (SOFOMORE). Secondly, we evaluate the SOFOMORE model by comparison with source reconstruction methods that use fixed forward models. Simulated and real EEG data demonstrate that invoking a stochastic forward model leads to improved source estimates.

Original language	English
Title of host publication	IEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009
Publisher	IEEE
Publication date	2009
Pages	1-6
ISBN (Print)	978-1-4244-4947-7
DOIs	https://doi.org/10.1109/MLSP.2009.5306189
Publication status	Published - 2009
Event	2009 IEEE International Workshop on Machine Learning for Signal Processing - Grenoble, France Duration: 1 Sept 2009 → 4 Sept 2009 Conference number: 19 https://ieeexplore.ieee.org/xpl/conhome/5290615/proceeding

Workshop

Workshop	2009 IEEE International Workshop on Machine Learning for Signal Processing
Number	19
Country/Territory	France
City	Grenoble
Period	01/09/2009 → 04/09/2009
Internet address	https://ieeexplore.ieee.org/xpl/conhome/5290615/proceeding

Bibliographical note

Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Access to Document

10.1109/MLSP.2009.5306189

StahlhutFinal published version, 4.78 MB

OpenUrl availability

Check availability in DTU Findit

Cite this

@inproceedings{e82e8ae69033433ea5d7f25fdf2ac25a,

title = "Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE)",

abstract = "In this paper we propose an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model is motivated by the many uncertain contributions that form the forward propagation model including the tissue conductivity distribution, the cortical surface, and electrode positions. We first present a hierarchical Bayesian framework for EEG source localization that jointly performs source and forward model reconstruction (SOFOMORE). Secondly, we evaluate the SOFOMORE model by comparison with source reconstruction methods that use fixed forward models. Simulated and real EEG data demonstrate that invoking a stochastic forward model leads to improved source estimates.",

author = "Carsten Stahlhut and Morten M{\o}rup and Ole Winther and Hansen, \{Lars Kai\}",

note = "Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; 2009 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2009 ; Conference date: 01-09-2009 Through 04-09-2009",

year = "2009",

doi = "10.1109/MLSP.2009.5306189",

language = "English",

isbn = "978-1-4244-4947-7",

pages = "1--6",

booktitle = "IEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009",

publisher = "IEEE",

address = "United States",

url = "https://ieeexplore.ieee.org/xpl/conhome/5290615/proceeding",

}

Stahlhut, C, Mørup, M , Winther, O & Hansen, LK 2009, Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE). in IEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE, pp. 1-6, 2009 IEEE International Workshop on Machine Learning for Signal Processing, Grenoble, France, 01/09/2009. https://doi.org/10.1109/MLSP.2009.5306189

Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE). / Stahlhut, Carsten; Mørup, Morten ; Winther, Ole et al.
IEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE, 2009. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE)

AU - Stahlhut, Carsten

AU - Mørup, Morten

AU - Winther, Ole

AU - Hansen, Lars Kai

N1 - Conference code: 19

PY - 2009

Y1 - 2009

N2 - In this paper we propose an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model is motivated by the many uncertain contributions that form the forward propagation model including the tissue conductivity distribution, the cortical surface, and electrode positions. We first present a hierarchical Bayesian framework for EEG source localization that jointly performs source and forward model reconstruction (SOFOMORE). Secondly, we evaluate the SOFOMORE model by comparison with source reconstruction methods that use fixed forward models. Simulated and real EEG data demonstrate that invoking a stochastic forward model leads to improved source estimates.

AB - In this paper we propose an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model is motivated by the many uncertain contributions that form the forward propagation model including the tissue conductivity distribution, the cortical surface, and electrode positions. We first present a hierarchical Bayesian framework for EEG source localization that jointly performs source and forward model reconstruction (SOFOMORE). Secondly, we evaluate the SOFOMORE model by comparison with source reconstruction methods that use fixed forward models. Simulated and real EEG data demonstrate that invoking a stochastic forward model leads to improved source estimates.

U2 - 10.1109/MLSP.2009.5306189

DO - 10.1109/MLSP.2009.5306189

M3 - Article in proceedings

SN - 978-1-4244-4947-7

SP - 1

EP - 6

BT - IEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009

PB - IEEE

T2 - 2009 IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 1 September 2009 through 4 September 2009

ER -

Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE)

Abstract

Workshop

Bibliographical note

Access to Document

OpenUrl availability

Fingerprint

Cite this