Neural activity reconstruction with MEG/EEG data considering noise regularization

CATHEGORY 2: The reconstruction of neural activity acquired with MEG/EEG devices (magnetoencephalogram/electroencephalogram) consists on generating three dimensional images indicating the location of the sources of activity. The neural activity is commonly modeled as current dipoles distributed over the cortical surface, for guaranteeing a linear propagation model though the head until the sensors placed on the scalp. There are several solution approaches used for estimating neural activity, they are mainly differentiated in the a priori information included and their sensibility to high noise levels. A comparison between different static solution approaches commonly used in the literature (minimum norm, LORETA, sLORETA) is presented in this paper. Their performance has been evaluated in different noise conditions with and without regularization for reducing uncertainty, being the general cross validation the best fitted regularization. Then it has been tested the effect of the number of dipoles used in the forward modeling; models with 5124, 8196 and 20484 dipoles were compared giving similar estimation errors but importance differences in computational effort were observed.