Electro-magnetic source imaging using realistic head models

Date

2005

Author

Akalın Acar, Zeynep

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Electro-Magnetic Source Imaging (EMSI) is the estimation of the position, orientation and strength of active electrical sources within the brain from electrical and magnetic measurements. For an accurate source localization, the head model must correctly represent the electrical and geometrical properties of the head. To solve the forward problem using realistic head models numerical techniques must be used. This work uses the Boundary Element Method (BEM) for solving the forward problem. The accuracy of the existing BEM formulation is improved by using second order elements, recursive integration and the isolated problem approach (IPA). Two new formulations are developed to improve the solution speed by computing transfer matrices for EEG and MEG solutions. The IPA formulation is generalized and integrated into the accelerated BEM algorithm. Once the transfer matrices are computed, the forward solutions take about 300 ms for a 256 sensor EEG and MEG system. The head model used in the BEM solutions is constructed by segmenting three dimensional multimodal magnetic resonance images. For segmentation, a semi-automatic hybrid algorithm is developed that makes use of snakes, morphological operations, thresholding and region growing. The mesh generation algorithm allows intersecting tissue compartments. For the inverse problem solution genetic algorithm (GA) is used to search for a given number of dipoles. Source localization with simulated data show that the localization error is within 1.1 mm for EEG and 1.2 mm for MEG when SNR is 10 on a realistic model with 7 compartments. When a single-dipole source in a realistic model is explored using a best-fit spherical model, the localization errors increase up to 8.5 mm for EEG and 7 mm for MEG. Similar tests are also performed with multiple dipoles. It was observed that realistic models provide definitely more accurate results

Subject Keywords

Bioengineering.

URI

http://etd.lib.metu.edu.tr/upload/2/12606173/index.pdf
https://hdl.handle.net/11511/15170

Collections

Graduate School of Natural and Applied Sciences, Thesis