Differential characterization of neural sources with the bimodal truncated SVD pseudo-inverse for EEG and MEG measurements


Gencer N. G. , WİLLİAMSON S. J.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, vol.45, no.7, pp.827-838, 1998 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 45 Issue: 7
  • Publication Date: 1998
  • Doi Number: 10.1109/10.686790
  • Title of Journal : IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
  • Page Numbers: pp.827-838
  • Keywords: electroencephalography (EEG), magnetic source imaging, magnetoencaphalography (MEG), singular value decomposition (SVD), truncated SVD pseudo-inversion, LEAST-SQUARES ESTIMATION, MAGNETIC SOURCE IMAGES, SPHERICAL MODEL, RECONSTRUCTION, LOCALIZATION, DIPOLE, BRAIN

Abstract

A method for obtaining a practical inverse for the distribution of neural activity in the human cerebral cortex is developed for electric, magnetic, and bimodal data to exploit their complementary aspects. Intracellular current is represented by current dipoles uniformly distributed on two parallel sulci joined by a gyrus, Linear systems of equations relate electric, magnetic, and binodal data to unknown dipole moments. The corresponding lead-field matrices are characterized by singular 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 decay behavior with increasing number of measurements, however, that property is useful depending on the noise in the measurements. The truncated SVD pseudo-inverse is used to control noise artifacts in the reconstructed images, Simulations for single-dipole sources at different depths reveal the relative contributions of electric and magnetic measures. For realistic noise levels the performance of both unimodal and bimodal systems do not improve with an increase in the number of measurements beyond similar to 100. Bimodal image reconstructions are generally superior to unimodal ones in finding the center of activity.