Title
Parametric surface-source modeling and estimation with electroencephalography.
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 Cramér-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.
Year
DOI
Venue
2006
10.1016/j.ics.2007.01.024
International Congress Series
Keywords
Field
DocType
spreading depression,parametric surface,eeg,electroencephalography
Parametric surface,Likelihood-ratio test,Computer science,Imaging phantom,Artificial intelligence,Electroencephalography,Source modeling,Computer vision,Mathematical optimization,Algorithm,Parametric statistics,Boundary element method,Dipole
Journal
Volume
Issue
ISSN
1300
12
0018-9294
Citations 
PageRank 
References 
2
0.45
15
Authors
5
Name
Order
Citations
PageRank
Nannan Cao120.45
Imam Samil Yetik215420.93
Arye Nehorai31257126.92
C. Muravchik454368.59
Jens Haueisen530553.60