Graduation Semester and Year
Summer 2024
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Mathematics
Department
Mathematics
First Advisor
Jianzhong Su
Second Advisor
Hristo V Kojouharov
Third Advisor
Ren-Cang Li
Fourth Advisor
Li Wang
Abstract
A central task for Neuroscience is to determine the location of electrical activity of neural origin inside the brain. Electrical signals can be recorded at a high resolution in time but low resolution in space, thus making it difficult to locate their source unambiguously. Electrical Source Imaging (ESI) is a particular framework for neural electrical source location; it is possible by modeling any additional information we may have about the electrical sources. For instance, minimal-norm estimators assume that the most plausible estimation is that with a lower norm. However, these estimators possess a low resolution in space.
In this work, we construct a novel ESI estimator incorporating binary anatomical data from pathologies observed in the post-mortem to improve its spatial resolution.
This work may be extended to similar types of binary data derived from fMRI, NIRS, and CT, among others.
Keywords
EEG, Source localization, Source reconstruction, Brain, Stroke, Inverse Problem, ADMM
Disciplines
Other Applied Mathematics
License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Recommended Citation
Enciso Alva, Julio Cesar, "New Methods in Electrical Source Imaging Based on EEG and Post-Mortem Pathology Data" (2024). Mathematics Dissertations. 162.
https://mavmatrix.uta.edu/math_dissertations/162