1 Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark2 Department of Informatics and Mathematical Modeling, Technical University of Denmark3 Department of Applied Mathematics and Computer Science, Technical University of Denmark4 University of California, San Francisco5 Convex Imaging6 Copenhagen Center for Health Technology, Center, Technical University of Denmark
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.