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 compared to spherical models. The EMSI approach is also tested using experimental EEG data to localize the sources of auditory evoked potentials. The reconstructed source locations are correctly found in the Heschl' / s gyrus. In conclusion, this thesis presents a complete source localization framework for future brain research using the EMSI.
Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/2/12606173/index.pdf |
Date | 01 June 2005 |
Creators | Akalin Acar, Zeynep |
Contributors | Gencer, Nevzat G. |
Publisher | METU |
Source Sets | Middle East Technical Univ. |
Language | English |
Detected Language | English |
Type | Ph.D. Thesis |
Format | text/pdf |
Rights | To liberate the content for METU campus |
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