DIFFERENTIAL CHARACTERIZATION OF NEURAL SOURCES WITH THE BIMODAL TRUNCATED SVD PSEUDO-INVERSE FOR EEG AND MEG MEASUREMENTS

A method for obtaining a practical inverse for the distribution of neu ral activity in the human cerebral cortex is developed for electric, m agnetic, and bimodal data to exploit their complementary aspects. Intr acellular current is represented by current dipoles uniformly distribu ted on two parallel sulci joined by a gyrus, Linear systems of equatio ns relate electric, magnetic, and binodal data to unknown dipole momen ts. The corresponding lead-field matrices are characterized by singula r value decomposition (SVD), The optimal reference electrode location for electric data is chosen on the basis of the decay behavior of the singular values. The singular values of these matrices show better dec ay behavior with increasing number of measurements, however, that prop erty is useful depending on the noise in the measurements. The truncat ed SVD pseudo-inverse is used to control noise artifacts in the recons tructed images, Simulations for single-dipole sources at different dep ths reveal the relative contributions of electric and magnetic measure s. For realistic noise levels the performance of both unimodal and bim odal systems do not improve with an increase in the number of measurem ents beyond similar to 100. Bimodal image reconstructions are generall y superior to unimodal ones in finding the center of activity.