Title :
Differential characterization of neural sources with the bimodal truncated SVD pseudo-inverse for EEG and MEG measurements
Author :
Gençer, Nevzat G. ; Williamson, Samuel J.
Author_Institution :
Dept. of Electr. & Electron. Eng., Middle East Tech. Univ., Ankara, Turkey
fDate :
7/1/1998 12:00:00 AM
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 bimodal 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 ∼100. Bimodal image reconstructions are generally superior to unimodal ones in finding the center of activity.
Keywords :
electroencephalography; inverse problems; magnetoencephalography; medical signal processing; neurophysiology; singular value decomposition; bimodal data; bimodal image reconstructions; bimodal truncated singular value decomposition pseudo-inverse; center of activity; current dipoles; electric data; gyrus; human cerebral cortex; intracellular current; lead-field matrices; magnetic data; neural activity; neural sources; noise artifacts; optimal reference electrode location; parallel sulci; practical inverse; reconstructed images; unknown dipole moments; Cerebral cortex; Electroencephalography; Equations; Humans; Image reconstruction; Linear systems; Magnetic moments; Magnetic noise; Matrix decomposition; Noise measurement; Cerebral Cortex; Electric Conductivity; Electroencephalography; Humans; Image Processing, Computer-Assisted; Least-Squares Analysis; Linear Models; Magnetoencephalography; Models, Neurological;
Journal_Title :
Biomedical Engineering, IEEE Transactions on