Title :
EEG dipole localization bounds and MAP algorithms for head models with parameter uncertainties
Author :
Radich, Bill M. ; Buckley, Kevin M.
Author_Institution :
Dept. of Electr. Eng., Minnesota Univ., Minneapolis, MN, USA
fDate :
3/1/1995 12:00:00 AM
Abstract :
The Cramer-Rao bound for unbiased dipole location estimation is derived under the assumption of a general head model parameterized by deterministic and stochastic parameters. The expression thus characterizes fundamental limits on EEG dipole localization performance due to the effects of both model uncertainty and statistical measurement noise. Expressions are derived for the cases of multivariate Gaussian and gamma distribution priors, and examples are given to illustrate the derived bounds when the radii and conductivities of a four-concentric sphere head model are allowed to be random. The joint MAP estimate of location/model parameters is then examined as a means of achieving robustness to deviations from an ideal head model. Random variations in both the multiple sphere radii and the layer conductivities are shown, via the stochastic Cramer-Rao bounds and Monte Carlo simulation of the MAP estimator, to have the most impact on localization performance in high SNR regions, where finite sample effects are not the limiting factors. This corresponds most often to spatial regions that are close to the scalp electrodes.
Keywords :
brain models; electroencephalography; Cramer-Rao bound; EEG dipole localization bounds; MAP algorithms; Monte Carlo simulation; deterministic parameters; finite sample effects; four-concentric sphere head model; gamma distribution priors; general head model; head models; multiple sphere radii; multivariate Gaussian distribution; parameter uncertainties; scalp electrodes; stochastic parameters; unbiased dipole location estimation; Brain modeling; Conductivity; Electroencephalography; Measurement uncertainty; Noise measurement; Robustness; Scalp; Stochastic processes; Stochastic resonance; Uncertain systems; Algorithms; Bayes Theorem; Electric Conductivity; Electroencephalography; Head; Humans; Models, Biological; Monte Carlo Method; Multivariate Analysis; Stochastic Processes;
Journal_Title :
Biomedical Engineering, IEEE Transactions on