Document Detail


Estimating brain conductivities and dipole source signals with EEG arrays.
MedLine Citation:
PMID:  15605858     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Techniques based on electroencephalography (EEG) measure the electric potentials on the scalp and process them to infer the location, distribution, and intensity of underlying neural activity. Accuracy in estimating these parameters is highly sensitive to uncertainty in the conductivities of the head tissues. Furthermore, dissimilarities among individuals are ignored when standarized values are used. In this paper, we apply the maximum-likelihood and maximum a posteriori (MAP) techniques to simultaneously estimate the layer conductivity ratios and source signal using EEG data. We use the classical 4-sphere model to approximate the head geometry, and assume a known dipole source position. The accuracy of our estimates is evaluated by comparing their standard deviations with the Cramér-Rao bound (CRB). The applicability of these techniques is illustrated with numerical examples on simulated EEG data. Our results show that the estimates have low bias and attain the CRB for sufficiently large number of experiments. We also present numerical examples evaluating the sensitivity to imprecise assumptions on the source position and skull thickness. Finally, we propose extensions to the case of unknown source position and present examples for real data.
Authors:
David Gutiérrez; Arye Nehorai; Carlos H Muravchik
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Publication Detail:
Type:  Comparative Study; Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  51     ISSN:  0018-9294     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2004 Dec 
Date Detail:
Created Date:  2004-12-20     Completed Date:  2005-01-11     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2113-22     Citation Subset:  IM    
Affiliation:
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607-7053, USA. dgutie7@uic.edu
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MeSH Terms
Descriptor/Qualifier:
Brain / physiology*
Brain Mapping / methods*
Computer Simulation
Diagnosis, Computer-Assisted / methods*
Electric Conductivity / diagnostic use*
Electroencephalography / methods*
Humans
Models, Neurological*
Models, Statistical
Reproducibility of Results
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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