Effects of geometric head model perturbations on the EEG forward and inverse problems

We study the effect of geometric head model perturbations on the electroencephalography (EEG) forward and inverse problems. Small magnitude perturbations of the shape of the head could represent uncertainties in the head model due to errors on images or techniques used to construct the model. They c...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2006-03, Vol.53 (3), p.421-429
Main Authors: von Ellenrieder, N., Muravchik, C.H., Nehorai, A.
Format: Article
Language:English
Subjects:
Brain - physiology
Brain Mapping - methods
Brain modeling
Computational geometry
Computer Simulation
CramÉr-Rao bound
Diagnosis, Computer-Assisted - methods
EEG
Electroencephalography
Electroencephalography - methods
Evoked Potentials - physiology
Head
Head - physiology
Humans
inverse problem
Inverse problems
Models, Biological
Models, Statistical
Noise measurement
Numerical Analysis, Computer-Assisted
Performance analysis
perturbations analysis
Reproducibility of Results
Sensitivity and Specificity
Shape
Solid modeling
stochastic modeling
Uncertainty
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Summary:We study the effect of geometric head model perturbations on the electroencephalography (EEG) forward and inverse problems. Small magnitude perturbations of the shape of the head could represent uncertainties in the head model due to errors on images or techniques used to construct the model. They could also represent small scale details of the shape of the surfaces not described in a deterministic model, such as the sulci and fissures of the cortical layer. We perform a first-order perturbation analysis, using a meshless method for computing the sensitivity of the solution of the forward problem to the geometry of the head model. The effect on the forward problem solution is treated as noise in the EEG measurements and the Crame/spl acute/r-Rao bound is computed to quantify the effect on the inverse problem performance. Our results show that, for a dipolar source, the effect of the perturbations on the inverse problem performance is under the level of the uncertainties due to the spontaneous brain activity. Thus, the results suggest that an extremely detailed model of the head may be unnecessary when solving the EEG inverse problem.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2005.869769