EMD-Based EEG and fMRI Data Analysis and Integration for High Precision Brain Functional Imaging

Moradi, Narges

EMD-Based EEG and fMRI Data Analysis and Integration for High Precision Brain Functional Imaging

dc.contributor.advisor	Sotero Diaz, Roberto
dc.contributor.advisor	Gordon Goodyear, Bradley
dc.contributor.author	Moradi, Narges
dc.contributor.committeemember	Protzner, Andrea
dc.contributor.committeemember	Wiebe, Samuel
dc.contributor.committeemember	Leung, Henry
dc.contributor.committeemember	Murari, Kartikeya
dc.contributor.committeemember	Hecker, Kenton
dc.contributor.committeemember	Meltzer, Jed
dc.date	2023-06
dc.date.accessioned	2023-05-05T15:11:40Z
dc.date.available	2023-05-05T15:11:40Z
dc.date.issued	2023-04-28
dc.description.abstract	To understand the spatiotemporal dynamics of brain function, both high spatial and temporal resolution imaging data are required. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two non-invasive and complementary functional brain imaging methods with high temporal and spatial resolution, respectively. Thus, combining EEG and fMRI data would permit mapping of brain function with high temporal and spatial resolution simultaneously. The accurate design of models for EEG-fMRI data integration has become an important research topic; however, to date, the success of these models has been limited. In this thesis, new analysis methods are proposed that combine mathematically decomposed components of EEG and fMRI data, guided by information about the underlying neural activity. Specifically, this thesis introduces and validates novel methods based on Empirical Mode Decomposition (EMD) to remove the global signal (GS) from fMRI, denoise the gamma frequency band of EEG, and to integrate EEG with fMRI data. EMD-based methods, ICEEDMAN and the 3D-EMD, are used to decompose EEG and fMRI data into temporal- and spatial-Intrinsic Mode Functions (IMFs) (TIMFs and SIMFs, respectively). First, we show that GS can be removed from the fMRI by removing low-frequency SIMFs causing spurious high global connectivity in the brain. Second, we denoise EEG Gamma-band by removing low-power TIMFs from its frequency- and amplitude-modulated components corresponding to the noise. Finally, we improve EEG source localization precision by adding fMRI’s high spatial-frequency-based weights to the EEG inverse problem’s gain matrix, thereby improving EEG's spatial resolution, especially when deep regions are involved. This thesis thus develops novel methods to increase the precision of imaging of brain function with less artifacts and high spatial and temporal resolutions. A more complete representation of brain function is crucial for better brain function realization, accurate diagnosis, and development of effective treatments for brain diseases such as epilepsy and ADHD. It could aid by specifying sources of seizures and distinguishing patterns and networks involved in ADHD and epileptic activities with high precision.
dc.identifier.citation	Moradi, N. (2023). EMD-based EEG and fMRI data analysis and integration for high precision brain functional imaging (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	http://hdl.handle.net/1880/116161
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/dspace/41006
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	EEG
dc.subject	fMRI
dc.subject	EEG and fMRI integration
dc.subject	EEG gamma band denoising
dc.subject	fMRI global signal
dc.subject	Deep brain mapping
dc.subject	Brain functional imaging
dc.subject	Empirical mode decomposition (EMD)
dc.subject.classification	Education--Health
dc.subject.classification	Neuroscience
dc.subject.classification	Biophysics
dc.title	EMD-Based EEG and fMRI Data Analysis and Integration for High Precision Brain Functional Imaging
dc.type	doctoral thesis
thesis.degree.discipline	Engineering – Biomedical
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.