EEG source localization: A neural network approach

Abstract Functional activity in the brain is associated with the generation of currents and resultant voltages which may be observed on the scalp as the electroencephelogram. The current sources may be modeled as dipoles. The properties of the current dipole sources may be studied by solving either the forward or inverse problems. The forward problem utilizes a volume conductor model for the head, in which the potentials on the conductor surface are computed based on an assumed current dipole at an arbitrary location, orientation, and strength. In the inverse problem, on the other hand, a current dipole, or a group of dipoles, is identified based on the observed EEG. Both the forward and inverse problems are typically solved by numerical procedures, such as a boundary element method and an optimization algorithm. These approaches are highly time-consuming and unsuitable for the rapid evaluation of brain function. In this paper we present a different approach to these problems based on machine learning. We solve both problems using artificial neural networks which are trained off-line using back-propagation techniques to learn the complex source-potential relationships of head volume conduction. Once trained, these networks are able to generalize their knowledge to localize functional activity within the brain in a computationally efficient manner. [Neurol Res 2001; 23: 457-464]

[1]  U. Kamil,et al.  Functional imaging , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Mingni Sun,et al.  An efficient algorithm for computing multishell spherical volume conductor models in EEG dipole source localization. , 1997, IEEE transactions on bio-medical engineering.

[3]  D. Cohen,et al.  Comparison of the magnetoencephalogram and electroencephalogram. , 1979, Electroencephalography and clinical neurophysiology.

[4]  Xiang-Gen Xia,et al.  Localizing functional activity in the brain through time-frequency analysis and synthesis of the EEG , 1996, Proc. IEEE.

[5]  G C YEH,et al.  THE POTENTIAL OF A GENERAL DIPOLE IN A HOMOGENEOUS CONDUCTING PROLATE SPHEROID , 1957, Annals of the New York Academy of Sciences.

[6]  Mingui Sun,et al.  The forward EEG solutions can be computed using artificial neural networks , 2000, IEEE Transactions on Biomedical Engineering.

[7]  J. Haueisen,et al.  Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head , 1997, IEEE Transactions on Biomedical Engineering.

[8]  W. Shankle,et al.  Approximating dipoles from human EEG activity: the effect of dipole source configuration on dipolarity using single dipole models , 1999, IEEE Transactions on Biomedical Engineering.

[9]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[10]  R. Leahy,et al.  EEG and MEG: forward solutions for inverse methods , 1999, IEEE Transactions on Biomedical Engineering.

[11]  J. D. Munck.  The potential distribution in a layered anisotropic spheroidal volume conductor , 1988 .

[12]  Thomas M. Strat,et al.  Decision analysis using belief functions , 1990, Int. J. Approx. Reason..

[13]  A network inversion technique for estimating equivalent dipole description of visual evoked potential. , 2000, Methods of information in medicine.

[14]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[15]  B. N. Cuffin,et al.  A method for localizing EEG sources in realistic head models , 1995, IEEE Transactions on Biomedical Engineering.

[16]  R D Sidman,et al.  A method for localization of sources of human cerebral potentials evoked by sensory stimuli. , 1978, Sensory processes.

[17]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[18]  E.-J. Speckmann,et al.  Introduction of the neurophysiological basis of the EEG and DC potentials , 1993 .

[19]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[20]  M. Scherg.  Fundamentals if dipole source potential analysis , 1990 .

[21]  Chi-Sang Poon,et al.  A new electrocardiography inverse solution by means of neural networks , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[22]  J. Meyer,et al.  Functional brain imaging. , 1995, Journal of neurology, neurosurgery, and psychiatry.