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- Creators: Berisha, Visar
Description
Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.
ContributorsDutta, Arindam (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Richmond, Christ (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018
Description
The research question this thesis aims to answer is whether depressive symptoms of adolescents involved in romantic relationships are related to their rejection sensitivity. It was hypothesized that adolescents who have more rejection sensitivity, indicated by a bigger P3b response, will have more depressive symptoms. This hypothesis was tested by having adolescent couples attend a lab session in which they played a Social Rejection Task while EEG data was being collected. Rejection sensitivity was measured using the activity of the P3b ERP at the Pz electrode. The P3b ERP was chosen to measure rejection sensitivity as it has been used before to measure rejection sensitivity in previous ostracism studies. Depressive symptoms were measured using the 20-item Center for Epidemiological Studies Depression Scale (CES-D, Radloff, 1977). After running a multiple regression analysis, the results did not support the hypothesis; instead, the results showed no relationship between rejection sensitivity and depressive symptoms. The results are also contrary to similar literature which typically shows that the higher the rejection sensitivity, the greater the depressive symptoms.
ContributorsBiera, Alex (Author) / Dishion, Tom (Thesis director) / Ha, Thao (Committee member) / Shore, Danielle (Committee member) / Barrett, The Honors College (Contributor)
Created2015-05
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 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