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

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    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 languageEnglish
    Title of host publicationIEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009
    PublisherIEEE
    Publication date2009
    Pages1-6
    ISBN (Print)978-1-4244-4947-7
    DOIs
    Publication statusPublished - 2009
    Event2009 IEEE International Workshop on Machine Learning for Signal Processing - Grenoble, France
    Duration: 2 Sep 20094 Sep 2009
    http://mlsp2009.conwiz.dk/

    Workshop

    Workshop2009 IEEE International Workshop on Machine Learning for Signal Processing
    CountryFrance
    CityGrenoble
    Period02/09/200904/09/2009
    Internet address

    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.

    Cite this

    Stahlhut, C., Mørup, M., Winther, O., & Hansen, L. K. (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 (pp. 1-6). IEEE. https://doi.org/10.1109/MLSP.2009.5306189
    Stahlhut, Carsten ; Mørup, Morten ; Winther, Ole ; Hansen, Lars Kai. / Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE). IEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE, 2009. pp. 1-6
    @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.",
    year = "2009",
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    language = "English",
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    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, 02/09/2009. 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; Hansen, Lars Kai.

    IEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE, 2009. p. 1-6.

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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    N1 - 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.

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    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.

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    Stahlhut C, Mørup M, Winther O, Hansen LK. 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. 2009. p. 1-6 https://doi.org/10.1109/MLSP.2009.5306189