Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


thesis_LH.pdf (15.83 MB)

ETD Abstract Container

Abstract Header

Bayesian Methods for Source Separation in Magnetoencephalography

Homa, Laura A
http://rave.ohiolink.edu/etdc/view?acc_num=case1365175207

Abstract Details

2013, Doctor of Philosophy, Case Western Reserve University, Applied Mathematics.
Magnetoencephalography (MEG) is a non-invasive brain imaging modality which localizes the sources of electrical activity within the brain based on measurements of the induced magnetic field outside the head. It has a variety of clinical applications; in particular, when a patient suffering from focal epilepsy is not responsive to drug treatment, the next step is often surgical intervention to remove the part of the brain where the seizures originate. MEG can potentially be used to localize the foci of the onset of seizures in order to assist with surgery planning. A great challenge in the MEG inverse problem is that the data are severely corrupted by noise generated by both independent external sources and biological noise sources within the brain itself. Therefore, it is of paramount interest in MEG to develop methods to distinguish between the signal generated by the sources of interest from that which arises from noise sources. We address the source separation problem within the Bayesian framework for both single time slice data and time series data. For single time slice data, we propose a mixture prior which incorporates the different statistical characteristics of the sources of interest and the noise sources. In addition, we propose a novel depth-scanning algorithm to identify and localize deep focal sources, overcoming the tendency of MEG inverse methods to explain all data with cortical sources. When considering source separation for time series data, we specifically address the problem of separating the signal of interest from the noise signal generated by spontaneous brain activity. It is well-known that this brain noise is correlated in both space and time. We take the novel approach of solving the MEG inverse problem using a Krylov subspace iterative method combined with statistically inspired left and right preconditioners. In particular, the left preconditioner is related to the covariance structure of the brain noise, while the right preconditioner is used to convey our prior beliefs about the statistical behavior of the unknown sources of interest.
Daniela Calvetti (Advisor)
Erkki Somersalo (Advisor)
Weihong Guo (Committee Member)
Dominique Durand (Committee Member)
204 p.
Mathematics

Recommended Citations

Citations

  • Homa, L. A. (2013). Bayesian Methods for Source Separation in Magnetoencephalography [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1365175207

    APA Style (7th edition)

  • Homa, Laura. Bayesian Methods for Source Separation in Magnetoencephalography. 2013. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1365175207.

    MLA Style (8th edition)

  • Homa, Laura. "Bayesian Methods for Source Separation in Magnetoencephalography." Doctoral dissertation, Case Western Reserve University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1365175207

    Chicago Manual of Style (17th edition)

case1365175207
280
© 2013, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.