@CONFERENCE\{IMM2009-05782,
    author       = "C. Stahlhut and M. M{\o}rup and O. Winther and L. K. Hansen",
    title        = "Hierarchical Bayesian Model for Simultaneous {EEG} Source and Forward Model Reconstruction (SOFOMORE)",
    year         = "2009",
    keywords     = "{EEG,} source reconstruction, stochastic forward model, distributed model",
    pages        = "1-6",
    booktitle    = "Machine Learning for Signal Processing (MLSP), {IEEE} Workshop on",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    note         = "DOI: 10.1109/MLSP.2009.5306189",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5782-full.html",
    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."
}