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Hierarchical Bayesian Approaches to EEG/MEG Source Reconstruction

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If you have a question about this talk, please contact Carola-Bibiane Schoenlieb.

In the last talk, I want to describe how hierarchical Bayesian modeling (HBM) is used to address various challenges arising from the inverse problem of Electroencephalography (EEG) / Magnetoencephalography (MEG) based source reconstruction: Measuring the induced electromagnetic fields at the head surface to estimate the underlying, activity-related ion currents in the brain is a challenging, severely ill-posed inverse problem. Due to the under-determinedness and the spatial characteristics of the direct problem, especially the recovery of brain networks involving deep-lying sources is still a challenging task for any inverse method. Apart from these theoretical problems, many practical challenges and uncertainties arise in the analysis of EEG /MEG data. However, the high temporal resolution of EEG /MEG recordings and their direct correspondence to the neuronal activity makes them indispensable tools for neuroimaging. I want to sketch how hierarchical Bayesian modeling (HBM) can be used as a convenient framework to address some of the above aspects and show own results on the performance of fully-Bayesian inference methods for HBM for source configurations consisting of few, focal sources when used with realistic, high resolution Finite Element (FE) head models.

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This talk is part of the Bayesian approach in inverse problems series.

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