Estimating brain conductivities and dipole source signals with EEG arrays

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 hea...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2004-12, Vol.51 (12), p.2113-2122
Main Authors: Gutierrez, D., Nehorai, A., Muravchik, C.H.
Format: Article
Language:English
Subjects:
Brain - physiology
Brain conductivities
Brain Mapping - methods
Brain modeling
Computer Simulation
Conductivity
CramÉr-Rao bound
Diagnosis, Computer-Assisted - methods
Electric Conductivity
Electric potential
Electric variables measurement
Electroencephalography
Electroencephalography - methods
Geometry
Humans
Maximum likelihood estimation
Models, Neurological
Models, Statistical
Parameter estimation
Reproducibility of Results
Scalp
Sensitivity and Specificity
sensor array processing
Solid modeling
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Summary: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 standardized 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 Crame/spl acute/r-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.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2004.836507