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
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Item Time-locked Cortical Processing of Speech in Complex Environments(2021) Kulasingham, Joshua Pranjeevan; Simon, Jonathan Z; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Our ability to communicate using speech depends on complex, rapid processing mechanisms in the human brain. These cortical processes make it possible for us to easily understand one another even in noisy environments. Measurements of neural activity have found that cortical responses time-lock to the acoustic and linguistic features of speech. Investigating the neural mechanisms that underlie this ability could lead to a better understanding of human cognition, language comprehension, and hearing and speech impairments. We use Magnetoencephalography (MEG), which non-invasively measures the magnetic fields that arise from neural activity, to further explore these time-locked cortical processes. One method for detecting this activity is the Temporal Response Function (TRF), which models the impulse response of the neural system to continuous stimuli. Prior work has found that TRFs reflect several stages of speech processing in the cortex. Accordingly, we use TRFs to investigate cortical processing of both low-level acoustic and high-level linguistic features of continuous speech. First, we find that cortical responses time-lock at high gamma frequencies (~100 Hz) to the acoustic envelope modulations of the low pitch segments of speech. Older and younger listeners show similar high gamma responses, even though slow envelope TRFs show age-related differences. Next, we utilize frequency domain analysis, TRFs and linear decoders to investigate cortical processing of high-level structures such as sentences and equations. We find that the cortical networks involved in arithmetic processing dissociate from those underlying language processing, although bothinvolve several overlapping areas. These processes are more separable when subjects selectively attend to one speaker over another distracting speaker. Finally, we compare both conventional and novel TRF algorithms in terms of their ability to estimate TRF components, which may provide robust measures for analyzing group and task differences in auditory and speech processing. Overall, this work provides insights into several stages of time-locked cortical processing of speech and highlights the use of TRFs for investigating neural responses to continuous speech in complex environments.Item THE ACOUSTIC QUALITIES THAT INFLUENCE AUDITORY OBJECT AND EVENT RECOGNITION(2019) Ogg, Mattson Wallace; Slevc, L. Robert; Neuroscience and Cognitive Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Throughout the course of a given day, human listeners encounter an immense variety of sounds in their environment. These are quickly transformed into mental representations of objects and events in the world, which guide more complex cognitive processes and behaviors. Through five experiments in this dissertation, I investigated the rapid formation of auditory object and event representations (i.e., shortly after sound onset) with a particular focus on understanding what acoustic information the auditory system uses to support this recognition process. The first three experiments analyzed behavioral (dissimilarity ratings in Experiment 1; duration-gated identification in Experiment 2) and neural (MEG decoding in Experiment 3) responses to a diverse array of natural sound recordings as a function of the acoustic qualities of the stimuli and their temporal development alongside participants’ concurrently developing responses. The findings from these studies highlight the importance of acoustic qualities related to noisiness, spectral envelope, spectrotemporal change over time, and change in fundamental frequency over time for sound recognition. Two additional studies further tested these results via syntheszied stimuli that explicitly manipulated these acoustic cues, interspersed among a new set of natural sounds. Findings from these acoustic manipulations as well as replications of my previous findings (with new stimuli and tasks) again revealed the importance of aperiodicity, spectral envelope, spectral variability and fundamental frequency in sound-category representations. Moreover, analyses of the synthesized stimuli suggested that aperiodicity is a particularly robust cue for some categories and that speech is difficult to characterize acoustically, at least based on this set of acoustic dimensions and synthesis approach. While the study of the perception of these acoustic cues has a long history, a fuller understanding of how these qualities contribute to natural auditory object recognition in humans has been difficult to glean. This is in part because behaviorally important categories of sound (studied together in this work) have previously been studied in isolation. By bringing these literatures together over these five experiments, this dissertation begins to outline a feature space that encapsulates many different behaviorally relevant sounds with dimensions related to aperiodicity, spectral envelope, spectral variability and fundamental frequency.Item Neural and computational approaches to auditory scene analysis(2015) Akram, Sahar; Shamma, Shihab A; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Our perception of the world is highly dependent on the complex processing of the sensory inputs by the brain. Hearing is one of those seemingly effortless sensory tasks that enables us to perceive the auditory world and integrate acoustic information from the environment into cognitive experiences. The main purpose of studying auditory system is to shed light on the neural mechanisms underlying our hearing ability. Understanding the systematic approach of the brain in performing such complicated tasks is an ultimate goal with numerous clinical and intellectual applications. In this thesis, we take advantage of various experimental and computational approaches to understand the functionality of the brain in analyzing complex auditory scenes. We first focus on investigating the behavioral and neural mechanisms underlying auditory sound segregation, also known as auditory streaming. Employing an informational masking paradigm, we explore the interaction between stimulus-driven and task-driven attentional process in the auditory cortex using magnetoencephalography (MEG) recordings from the human brain. The results demonstrate close links between perceptual and neural consequences of the auditory stream segregation, suggesting the neural activity to be viewed as an indicator of the auditory streaming percept. We examine more realistic auditory scenarios consisted of two speakers simultaneously present in an auditory scene and introduce a novel computational approach for decoding the attentional state of listeners in such environment. The proposed model focuses on an efficient implementation of a decoder for tracking the cognitive state of the brain, inspired from neural representation of auditory objects in the auditory cortex. The structure is based on an state-space model with the recorded MEG signal and individual speech envelopes as the input and the probability of attending to the target speaker as the output of the model. The proposed approach benefits from accurate and highly resolved estimation of attentional state in time as well as the inherent model-based dynamic denoising of the underlying state-space model, which makes it possible to reliably decode the attentional state under very low SNR conditions. As part of this research work, we investigate the neural representation of ambiguous auditory stimuli at the level of the auditory cortex. In perceiving a typical auditory scene, we may receive incomplete or ambiguous auditory information from the environment. This can lead to multiple interpretations of the same acoustic scene and formation of an ambitious perceptual state in the brain. Here, in a series of experimental studies, we focus on a particular example of ambitious stimulus (ambitious Shepard tone pair) and investigate the neural correlates of the contextual effect and perceptual biasing using MEG. The results from psychoacoustic and neural recordings suggest a set of hypothesis about the underlying neural mechanism of short-term memory and expectation modulation in the nervous system.Item Understanding Neuroplastic Effects of Transcranial Direct Current Stimulation through Analysis of Dynamics of Large-Scale Brain Networks(2012) Venkatakrishnan, Anusha; Contreras-Vidal, José L.; Neuroscience and Cognitive Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Intrinsic adult neuroplasticity plays a critical role in learning and memory as well as mediating functional recovery from brain lesions like stroke and traumatic brain injuries. Extrinsic strategies to aid favorable modulation of neuroplasticity act as important adjunctive tools of neurorehabilitation. Transcranial direct current stimulation (tDCS) is an example of a non-invasive technique that can successfully induce neuroplastic changes in the human brain, although the underlying mechanisms are not completely understood. In this regard, characterization of neuroplastic changes in large-scale brain networks is a functional and necessary step towards non-invasively understanding neuroplastic modulation mediated by tDCS in humans. This dissertation, thus, aimed to understand the effects of tDCS, on large-scale brain network dynamics recorded through magnetoencephalography (MEG) through three specific aims that will provide novel insights into the mechanism(s) through which plastic changes are promoted by tDCS, specifically in the context motor learning. This dissertation pursued a systematic investigation of these changes in whole-head cortical dynamics using both model-free and model-based analysis techniques. Two experiments were conducted to dissociate between network changes mediated by tDCS at rest as well as when coupled with a task in order to determine optimal conditions for using tDCS for clinical purposes. Results from Study 1 using model-free analysis showed that a specific fronto-parietal network at rest was modulated up to a period of 30 minutes outlasting the duration of the stimulation. Further model-based analysis of this fronto-parietal network showed that these differences were driven by network activity primarily involving high frequency gamma band connectivity to and from the supplementary motor area to associated regions (left primary motor cortex (stimulated region), left prefrontal and parietal cortices). Results from Study 2 showed that the tDCS exerts highly polarity-specific effects on the impact of oscillatory network connectivity, within the functionally relevant fronto-parietal network, on behavioral changes associated with motor learning. These results advance our understanding of neuroplasticity mediated by tDCS and thus, have implications in the clinical use of tDCS for enhancing efficacy of neurorehabilitation in patients with stroke and traumatic brain injury.Item Multi-Level Audio-Visual Interactions in Speech and Language Perception(2011) Rhone, Ariane E.; Idsardi, William J; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)That we perceive our environment as a unified scene rather than individual streams of auditory, visual, and other sensory information has recently provided motivation to move past the long-held tradition of studying these systems separately. Although they are each unique in their transduction organs, neural pathways, and cortical primary areas, the senses are ultimately merged in a meaningful way which allows us to navigate the multisensory world. Investigating how the senses are merged has become an increasingly wide field of research in recent decades, with the introduction and increased availability of neuroimaging techniques. Areas of study range from multisensory object perception to cross-modal attention, multisensory interactions, and integration. This thesis focuses on audio-visual speech perception, with special focus on facilitatory effects of visual information on auditory processing. When visual information is concordant with auditory information, it provides an advantage that is measurable in behavioral response times and evoked auditory fields (Chapter 3) and in increased entrainment to multisensory periodic stimuli reflected by steady-state responses (Chapter 4). When the audio-visual information is incongruent, the combination can often, but not always, combine to form a third, non-physically present percept (known as the McGurk effect). This effect is investigated (Chapter 5) using real word stimuli. McGurk percepts were not robustly elicited for a majority of stimulus types, but patterns of responses suggest that the physical and lexical properties of the auditory and visual stimulus may affect the likelihood of obtaining the illusion. Together, these experiments add to the growing body of knowledge that suggests that audio-visual interactions occur at multiple stages of processing.Item da Vinci's Encephalogram: In search of significant brain signals(2005-12-15) Ahmar, Nayef Elian; Simon, Jonathan Z; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Magnetoencephalography is a noninvasive tool that measures the magnetic activity of the brain. Its high temporal resolution makes it useful for studying auditory and speech models. However, it suffers from poor signal to noise ratio caused by corruption from non-stationary external noise, biological artifacts, and non-auditory neural noise in the brain. We remove external noise from neural channels using a frequency domain block least mean square adaptive filter with the help of three reference sensors that measure environmental noise alone. Significance tests that build on F-statistics present ample evidence of the benefit of such de-noising by increasing the number of significant channels and reducing the variability of false positives. Finally, the least significant and noisiest channel is filtered and used to de-noise neural signals while minimizing interference with the auditory signal. We propose a method for finding such reference channels and assess performance through receiver operating characteristics and statistical significance.