In the human brain, electrical activities are created due to the body functions. These
electrical activities create potentials and magnetic fields which can be monitored elec-
trically (Electroencephalography - EEG) or magnetically (Magnetoencephalography -
MEG). Electrical activities in human brain are usually modeled by electrical dipoles.
The purpose of Electro-magnetic source imaging (EMSI) is to determine the position,
orientation and strength of dipoles. The first stage of EMSI is to model the human
head numerically. In this study, The Finite Element Method (FEM) is chosen to han-
dle anisotropy in the brain. The second stage of EMSI is to solve the potentials and
magnetic fields for an assumed dipole configuration (forward problem). Realistic con-
ductivity distribution of human head is required for more accurate forward problem
solutions. However, to our knowledge, conductivity distribution for an individual has
not been computed yet.
The aim of this thesis study is to investigate the feasibility of a new approach to
update the initially assumed conductivity distribution by using the evoked potentials
and fields acquired during EMSI studies. This will increase the success of source
localization problem, since more realistic conductivity distribution of the head will be
used in the forward problem. This new method can also be used as a new imaging
modality, especially for inhomogeneities where the conductivity value deviates.
In this thesis study, to investigate the sensitivity of measurements to conductivity
perturbations, a FEM based sensitivity matrix approach is used. The performance
of the proposed method is tested using three different head models - homogeneous
spherical, 4 layer concentric sphere and realistic head model. For spherical head models
rectangular grids are preferred in the middle and curved elements are used nearby
the head boundary. For realistic cases, head models are developed using uniform
grids. Tissue boundary information is obtained by applying segmentation algorithms
to the Magnetic Resonance (MR) images. A paralel computer cluster is employed to
assess the feasibility of this new approach. PETSc library is used for forward problem
calculations and linear system solutions.
The performance of this novel approach depends on many factors such as the head
model, number of dipoles and sensors used in the calculation, noise in the measure-
ments, etc. In this thesis study, a number of simulations are performed to investigate
the effects of each of these parameters. Increase in the number of elements in the
head model leads to the increase in the number of unknows for linear system solu-
tions. Then, accuracy of the solution is improved with increased number of dipoles
or sensors. The performance of the adopted approach is investigated using noise-free
measurements as well as noisy measurements. For EEG, measurement noise decreases the accuracy
of the approach. For MEG, the effect of measurement noise is more pronounced and may lead to a larger
error in tissue conductivity calculation.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12609828/index.pdf |
| Date | 01 September 2008 |
| Creators | Yurtkolesi, Mustafa |
| Contributors | Gencer, Nevzat Guneri |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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