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S. Baillet, L. Garnero. A Bayesian Approach to Introducing Anatomo-Functional Priors in the EEG/MEG Inverse Problem. Biomedical Engineering, 44(5):374-385, May 1997.


In this paper, we present a new approach to the recovering of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) and electroencephalographic (EEG) imaging. This method consists in introducing spatial and temporal a priori information as a cure to this ill-posed inverse problem. A nonlinear spatial regularization scheme allows the preservation of dipole moment discontinuities between some a priori noncorrelated sources, for instance, when considering dipoles located on both sides of a sulcus. Moreover, we introduce temporal smoothness constraints on dipole magnitude evolution at time scales smaller than those of cognitive processes. These priors are easily integrated into a Bayesian formalism, yielding a maximum a posteriori (MAP) estimator of brain electrical activity. Results from EEG simulations of our method are presented and compared with those of classical quadratic regularization and a now popular generalized minimum-norm technique called low-resolution electromagnetic tomography (LORETA)


S. Baillet
L. Garnero

BibTex Reference

   Author = {Baillet, S. and Garnero, L.},
   Title = {A Bayesian Approach to Introducing Anatomo-Functional Priors in the {EEG}/{MEG} Inverse Problem},
   Journal = {Biomedical Engineering},
   Volume = {    44},
   Number = {5},
   Pages = {374--385},
   Month = {May},
   Year = {1997}

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