DocumentCode :
833241
Title :
Parametric Surface-Source Modeling and Estimation With Electroencephalography
Author :
Nannan Cao ; Yetik, I.S. ; Nehorai, A. ; Muravchik, Carlos H. ; Haueisen, J.
Author_Institution :
Dept. of Electr. & Syst. Eng., Washington Univ., St. Louis, MO
Volume :
53
Issue :
12
fYear :
2006
Firstpage :
2414
Lastpage :
2424
Abstract :
Electroencephalography (EEG) is an important tool for studying the brain functions and is becoming popular in clinical practice. In this paper, we develop four parametric EEG models to estimate current sources that are spatially distributed on a surface. Our models approximate the source shape and extent explicitly and can be applied to localize extended sources which are often encountered, e.g., in epilepsy diagnosis. We assume a realistic head model and solve the EEG forward problem using the boundary element method. We present the source models with increasing degrees of freedom, provide the forward solutions, and derive the maximum-likelihood estimates as well as Crameacuter-Rao bounds of the unknown source parameters. In order to evaluate the applicability of the proposed models, we first compare their estimation performances with the dipole model´s using several known source distributions. We then discuss the conditions under which we can distinguish between the proposed extended sources and the focal dipole using the generalized likelihood ratio test. We also apply our models to the electric measurements obtained from a phantom body in which an extended electric source is imbedded. We observe that the proposed model can capture the source extent information satisfactorily and the localization accuracy is better than the dipole model
Keywords :
boundary-elements methods; diseases; electroencephalography; maximum likelihood estimation; phantoms; physiological models; Cramer-Rao bounds; EEG; EEG forward problem; boundary element method; brain functions; current sources; electroencephalography; epilepsy diagnosis; generalized likelihood ratio; maximum-likelihood estimates; parametric surface-source modeling; phantom; source extent information; Boundary element methods; Brain modeling; Electric variables measurement; Electroencephalography; Epilepsy; Imaging phantoms; Maximum likelihood estimation; Performance evaluation; Shape; Testing; Cramér-Rao bounds; EEG; extended source modeling; likelihood ratio test; Action Potentials; Algorithms; Brain; Computer Simulation; Diagnosis, Computer-Assisted; Electroencephalography; Head; Humans; Models, Neurological; Surface Properties;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2006.883741
Filename :
4015589
Link To Document :
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