Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Advances in Quantitative Characterizations of Electrophysiological Neural Activity(2020) Nahmias, David; Kontson, Kimberly L; Simon, Jonathan Z; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Disorders of the brain and nervous system result in more hospitalizations and lost productivity than any other disease group. Electroencephalography (EEG), which measures brain electrical signals from the scalp, is a common neuro-monitoring technique used for diagnostic, rehabilitative, and therapeutic purposes. Understanding EEG quantitatively and its neural correlates with patient characteristics could inform the safety and efficacy of technologies that rely on EEG. In this dissertation, a large clinical data set comprised of over 35,000 recordings as well as data from previous research experiments are utilized to better quantify characteristics of neurological activity. We first propose non-parametric methods of evaluating consistency of quantitative EEG features (qEEG) by applying novel statistical approaches. These results provide data-driven methods of identifying qEEG and their spatial characteristics ideal for various applications, and determining consistencies of novel features using existing data. These qEEG are commonly used in feature-based machine learning applications. Further, EEG-driven deep learning has shown promising results in distinguishing recordings of subjects. To better understand the performance of these two machine learning approaches, we assess their ability to distinguish between subjects taking different anticonvulsants. Our methods could successfully discriminate between patients taking either anticonvulsant and those taking no medications solely from neural activity with similar performance from both feature-based and deep learning approaches. With feature-based methods, it is easier to interpret which qEEG have the most impact on algorithm performance. However, deep learning applications in EEG can present difficulty in understanding and investigating underlying neurophysiological implications. We propose and validate a method to investigate frequency band importance in EEG-driven deep learning models. The easy perturbation EEG algorithm for spectral importance (easyPEASI) is simpler than previous methods and is applied to classifications investigated in this work. Until this point, our work used well segmented EEG from clinical settings. However, EEG is usually corrupted by noise which can degrade its utility. We formulate and validate novel approaches to score electrophysiological signal quality based on the presence of noise from various sources. Further, we apply our method to compare and evaluate the performance of existing artifact removal algorithms.Item FOREST LOSS AND FRAGMENTATION IN SOUTHERN BAHIA, BRAZIL: IMPLICATIONS FOR THE EXTINCTION RISK OF GOLDEN-HEADED LION TAMARINS (Leontopithecus chrysomelas)(2011) Zeigler, Sara Lynn; Dubayah, Ralph; Fagan, William F; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Golden-headed lion tamarins (GHLTs; Leontopithecus chrysomelas) are Endangered arboreal primates endemic to the Atlantic Forest of Brazil, where continuing loss of forest and its connectivity are major threats. The objectives of my research were to assess the vulnerability of GHLTs to habitat loss, fragmentation, and threats related to small population size in the context of past, current, and future trends in range-wide forest cover in Brazil's Atlantic Forest. I did this by conducting a supervised classification of Landsat 5TM remotely-sensed imagery to define past and current forest cover in the region, analyzing connectivity patterns in a graph theoretical framework, projecting recent deforestation patterns into the future using a multi-layer perceptron neural network, and modeling GHLT metapopulaton viability using population viability analysis. I found that forest cover has declined throughout the range of the species by 13% over the last 20 years, and only one habitat patch is large enough on its own to support a genetically viable GHLT population able to recover from extrinsic threats such as fire and disease. Functional landscape connectivity, which is important for population persistence, acquisition of resources, and maintenance of genetic diversity, is low at the distance and movement cost thresholds likely associated with this arboreal species that is rarely seen in non-forest matrix. The majority of remaining forest cover throughout the species' range is found in patches that are either (1) too small to support even a single group of GHLTs or (2) found at low elevations, in areas of high human population density, or in close proximity to previously cleared areas--conditions that are associated with past deforestation patterns and that make current habitat vulnerable to loss. Finally, I found that many of the known GHLT populations have a moderate to high risk of local extinction even over short time scales and assuming no further forest loss, and their presence may represent extinction debt. Continued deforestation will accelerate population declines and local extinction events. The results of my dissertation research suggest that GHLTs and their habitat face significant threats and low viability in the future because of both ultimate and proximate drivers of extinction.