Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Imaging electrical conductivity distribution of the human head using evoked fields and potentials
Download
index.pdf
Date
2008
Author
Yurtkölesi, Mustafa
Metadata
Show full item record
Item Usage Stats
194
views
43
downloads
Cite This
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.
Subject Keywords
Electrical engineering.
,
Electricity and magnetism.
URI
http://etd.lib.metu.edu.tr/upload/12609828/index.pdf
https://hdl.handle.net/11511/17750
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Parallel implementation of the boundary element method for electromagnetic source imaging of the human brain
Ataseven, Yoldaş; Gençer, Nevzat Güneri; Department of Electrical and Electronics Engineering (2005)
Human brain functions are based on the electrochemical activity and interaction of the neurons constituting the brain. Some brain diseases are characterized by abnormalities of this activity. Detection of the location and orientation of this electrical activity is called electro-magnetic source imaging (EMSI) and is of signi cant importance since it promises to serve as a powerful tool for neuroscience. Boundary Element Method (BEM) is a method applicable for EMSI on realistic head geometries that generates...
Prototype Hardware Design for Brain Computer Interface Applications
Erdogan, Balkar; Akinci, Berna; Acar, Erman; Usakli, Ali Buelent; Gençer, Nevzat Güneri (2009-01-01)
Brain Computer Interface (BCI) is an alternative communication pathway between the human brain and outside world in which only the brain activity is interpreted in a special way. These systems are based on the electrical activity of the brain that can be measured via Electroencephalography (EEG) devices. BCI enables people with severe motor disorders (like ALS) to communicate with their environment or control a wheelchair for their movement by using the EEG signals. In this study, a prototype data acquisito...
High resolution imaging of anisotropic conductivity with magnetic resonance electrical impedance tomography (mr-eit)
Değirmenci, Evren; Eyüboğlu, Behçet Murat; Department of Electrical and Electronics Engineering (2010)
Electrical conductivity of biological tissues is a distinctive property which differs among tissues. It also varies according to the physiological and pathological state of tissues. Furthermore, in order to solve the bioelectric field problems accurately, electrical conductivity information is essential. Magnetic Resonance Electrical Impedance Tomography (MREIT) technique is proposed to image this information with high spatial resolution. However, almost all MREIT algorithms proposed to date assumes isotrop...
Wireless Monitoring of ECG Signal in Infants Using SWM and DWT Techniques
Rashid, Haroon; Qadir, Zakria; Zia, Moaz; Nesimoglu, Tayfun (2018-11-02)
Human heart cardiac muscles activities can be represented graphically using electrical impulses. Electrocardiography (ECG) signals are very important for physicians to diagnose heart disease. In this study, Bluetooth device is used to transmit data wirelessly. For this purpose, the generated signal from heart beat sensor is analyzed in MATLAB using Support Vector Machine (SVM) and Discrete Wavelet Transform (DWT) techniques. ECG Simulator compares the actual signal of infants with the reference signal and n...
Statistical Approaches for the Analysis of Dependency Among Neurons Under Noise
Gençağa, Deniz; Ayan, Sevgi Şengül; Farnoudkia, Hajar; Okuyucu, Serdar (MDPI AG, 2020-3-28)
Neuronal noise is a major factor affecting the communication between coupled neurons. In this work, we propose a statistical toolset to infer the coupling between two neurons under noise. We estimate these statistical dependencies from data which are generated by a coupled Hodgkin–Huxley (HH) model with additive noise. To infer the coupling using observation data, we employ copulas and information-theoretic quantities, such as the mutual information (MI) and the transfer entropy (TE). Copulas and MI between...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. Yurtkölesi, “Imaging electrical conductivity distribution of the human head using evoked fields and potentials,” M.S. - Master of Science, Middle East Technical University, 2008.