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 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 Post-Stroke Acute Dysexecutive Syndrome, a Disorder Resulting from Minor Stroke due to Disruption of Network Dynamics - Dataset(2020) Marsh, Elisabeth B.; Brodbeck, Christian; Llinas, Rafael H.; Mallick, Dania; Kulasingham, Joshua P.; Llinas, Rodolfo R.; Simon, Jonathan Z.Stroke patients with small CNS infarcts often demonstrate an acute dysexecutive syndrome characterized by difficulty with attention, concentration, and processing speed, independent of lesion size or location. We use magnetoencephalography (MEG) to show that disruption of network dynamics may be responsible. Nine patients with recent minor stroke and 8 age-similar controls underwent cognitive screening using the Montreal Cognitive Assessment (MoCA) and MEG to evaluate differences in cerebral activation patterns. During MEG, subjects participated in a visual picture-word matching task. Task complexity was increased as testing progressed. Cluster based permutation tests determined differences in activation patterns within the visual cortex, fusiform gyrus, and lateral temporal lobe. At visit 1, MoCA scores were significantly lower for patients than controls (median (IQR)=26.0 (4) versus 29.5 (3), p=0.005), and patient reaction times were increased. The amplitude of activation was significantly lower after infarct and demonstrated a pattern of temporal dispersion independent of stroke location. Differences were prominent in the fusiform gyrus and lateral temporal lobe. The pattern suggests that distributed network dysfunction may be responsible. Additionally, controls were able to modulate their cerebral activity based on task difficulty. In contrast, stroke patients exhibited the same low-amplitude response to all stimuli. Group differences remained, to a lesser degree, six months later; while MoCA scores and reaction times improved for patients. This study suggests that function is a globally distributed property beyond area-specific functionality, and illustrates the need for longer-term follow-up studies to determine whether abnormal activation patterns ultimately resolve or another mechanism underlies continued recovery.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 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.