@TECHREPORT\{IMM2010-06020,
    author       = "C. Stahlhut and D. Wipf and H. T. Attias and L. K. Hansen and S. S. Nagarajan",
    title        = "Probabilistic Algorithm for Electromagnetic Brain Imaging with Spatio-Temporal and Forward Model Priors",
    year         = "2010",
    month        = "jun",
    pages        = "1-14",
    number       = "",
    series       = "",
    institution  = "Technical University of Denmark, Department of Informatics and Mathematical Modelling",
    address      = "Richard Petersens Plads, {DK-}2800 Kgs. Lyngby, Denmark",
    type         = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6020-full.html",
    abstract     = "In this paper we present a novel spatio-temporal inverse method for solving the inverse M/{EEG} problem. The contribution is two-folded; firstly, the proposed model allows for a sparse spatial and temporal source representation of the M/{EEG} by applying an automatic relevance determination type prior. The utility of a sparse spatio-temporal representation is based on the assumption that the underlying source activity is indeed sparse and smooth in time. Secondly, we seek to reduce the influence of forward model errors on the source estimates, by applying a stochastic forward model. Applying a stochastic forward model is motivated by the random noise contributions such as the geometry of the cortical surface and the electrode positions.
Simulated data provide evidence that the spatio-temporal model leads to improved source estimates, especially at low signal-to-noise ratios, which is often the case in M/EEG."
}