Title :
Estimating brain conductivities and dipole source signals with EEG arrays
Author :
Gutiérrez, David ; Nehorai, Arye ; Muravchik, Carlos H.
Author_Institution :
Dept. of Bioeng., Illinois Univ., Chicago, IL, USA
Abstract :
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´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.
Keywords :
bioelectric potentials; biological tissues; brain models; electroencephalography; maximum likelihood estimation; medical signal processing; neurophysiology; 4-sphere model; Cramer-Rao bound; EEG arrays; brain conductivities; dipole source position; dipole source signals; electric potentials; electroencephalography; head geometry; head tissues; layer conductivity ratios estimation; maximum a posteriori technique; maximum-likelihood technique; neural activity; parameter estimation; scalp; skull thickness; source signal estimation; Brain modeling; Conductivity; Electric potential; Electric variables measurement; Electroencephalography; Geometry; Maximum likelihood estimation; Parameter estimation; Scalp; Solid modeling; Brain conductivities; CramÉr-Rao bound; electroencephalography; maximum-likelihood estimation; parameter estimation; sensor array processing; Brain; Brain Mapping; Computer Simulation; Diagnosis, Computer-Assisted; Electric Conductivity; Electroencephalography; Humans; Models, Neurological; Models, Statistical; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2004.836507