A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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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 On the gradual deployment of random pairwise key distribution schemes(2010-07-31) Yagan, Osman; Makowski, Armand M.In the context of wireless sensor networks, the pairwise key distribution scheme of Chan et al. has several advantages over other key distribution schemes including the original scheme of Eschenauer and Gligor. However, this offline pairwise key distribution mechanism requires that the network size be set in advance, and involves all sensor nodes simultaneously. Here, we address this issue by describing an implementation of the pairwise scheme that supports the gradual deployment of sensor nodes in several consecutive phases. We discuss the key ring size needed to maintain the secure connectivity throughout all the deployment phases. In particular we show that the number of keys at each sensor node can be taken to be O(log n) in order to achieve secure connectivity (with high probability).Item On random graphs associated with a pairwise key distribution scheme(2010-01-01) Yagan, Osman; Makowski, Armand M.The pairwise key distribution scheme of Chan et al. was proposed as an alternative to the key distribution scheme of Eschenauer and Gligor to enable network security in wireless sensor networks. We consider the random graph induced by this pairwise scheme under the assumption of full visibility, and show the existence of a zero-one law for graph connectivity.