Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Electroencephalography Feature Extraction of Neural Stimuli

Description
Many mysteries still surround brain function, and yet greater understanding of it is vital to advancing scientific research. Studies on the brain in particular play a huge role in the medical field as analysis can lead to proper diagnosis of patients and to anticipatory treatments. The objective of this research…

Many mysteries still surround brain function, and yet greater understanding of it is vital to advancing scientific research. Studies on the brain in particular play a huge role in the medical field as analysis can lead to proper diagnosis of patients and to anticipatory treatments. The objective of this research was to apply signal processing techniques on electroencephalogram (EEG) data in order to extract features for which to quantify an activity performed or a response to stimuli. The responses by the brain were shown in eigenspectrum plots in combination with time-frequency plots for each of the sensors to provide both spatial and temporal frequency analysis. Through this method, it was revealed how the brain responds to various stimuli not typically used in current research. Future applications might include testing similar stimuli on patients with neurological diseases to gain further insight into their condition.
ContributorsJackson, Matthew Joseph (Author) / Bliss, Daniel (Thesis director) / Berisha, Visar (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Representation Learning for Graph Structured Data using Deep Neural Networks

Description
Dealing with relational data structures is central to a wide-range of applications including social networks, epidemic modeling, molecular chemistry, medicine, energy distribution, and transportation. Machine learning models that can exploit the inherent structural/relational bias in the graph structured data have gained prominence in recent times. A recurring idea that appears…

Dealing with relational data structures is central to a wide-range of applications including social networks, epidemic modeling, molecular chemistry, medicine, energy distribution, and transportation. Machine learning models that can exploit the inherent structural/relational bias in the graph structured data have gained prominence in recent times. A recurring idea that appears in all approaches is to encode the nodes in the graph (or the entire graph) as low-dimensional vectors also known as embeddings, prior to carrying out downstream task-specific learning. It is crucial to eliminate hand-crafted features and instead directly incorporate the structural inductive bias into the deep learning architectures. In this dissertation, deep learning models that directly operate on graph structured data are proposed for effective representation learning. A literature review on existing graph representation learning is provided in the beginning of the dissertation. The primary focus of dissertation is on building novel graph neural network architectures that are robust against adversarial attacks. The proposed graph neural network models are extended to multiplex graphs (heterogeneous graphs). Finally, a relational neural network model is proposed to operate on a human structural connectome. For every research contribution of this dissertation, several empirical studies are conducted on benchmark datasets. The proposed graph neural network models, approaches, and architectures demonstrate significant performance improvements in comparison to the existing state-of-the-art graph embedding strategies.
ContributorsShanthamallu, Uday Shankar (Author) / Spanias, Andreas (Thesis advisor) / Thiagarajan, Jayaraman J (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2021