MS, University of Cincinnati, 2016, Engineering and Applied Science: Computer Engineering

Electroencephalography (EEG) is the measurement of brainwaves present on the scalp. It has existed for many years, but recently the technology has greatly advanced, transforming the field of EEG research. Wearable wireless electroencephalography devices are now being designed, making ambulatory EEG possible. Smaller and lighter electronics allow the user to wear systems continuously for long periods of time. Devices exist on the commercial market, but are expensive and restrictive in the ability to modify or adapt them to suit individual research needs. Thus, these commercial systems often do not satisfy the cost and/or the performance requirements of small research groups or research groups that have a diverse set of experimental setup needs. The purpose of this research is to design and implement an EEG platform for research, capable of high resolution recording, mobile use, and event related potentials analysis. The design process and the current state of the system is presented. Comparisons to existing devices and preliminary testing demonstrate that this device capable of recording high resolution EEG and is a valid test platform moving forward.

Committee: Fred Beyette Ph.D. (Committee Chair); Carla Purdy Ph.D. (Committee Member); Philip Wilsey Ph.D. (Committee Member) Subjects: Computer Engineering

PHD, Kent State University, 2013, College of Arts and Sciences / School of Biomedical Sciences

MAMONE, BERNADETT, M.ED. AUGUST 2013 BIOMEDICAL SCIENCES MOTOR IMAGERY TRAINING FACILITATES NEURAL ADAPTATIONS ASSOCIATED WITH MUSCLE STRENGTHENING IN AGING (208 PP.) Director of Thesis: Guang, H. Yue Background. Aging is accompanied by a decline in muscle strength. However, the underlying mechanism is not fully understood. Growing evidence indicates that voluntary muscle strength training paradigms enhance descending cortical drive and strengthen muscle output in the elderly. Motor Imagery Training (MIT) may improve strength, however, it is not known if MIT can reverses aging-related maladaptive changes and how the underpinnings differ from those of conventional strength training (CST). Using Transcranial Magnetic Stimulation (TMS) and Electroencephalography (EEG), we studied the relation between training-related motor cortical changes and muscle strengthening in the elderly. Methods. Thirty-two right-handed, healthy older adults (74.41±7.09 yrs) were randomly assigned to either an 8-week MIT (n=15) or CST (n=10) group for left elbow flexion (EF) strengthening. A non-training elderly control (n=7) and twenty non-training young (23±4.02 yrs) subjects were also included. We measured maximal left arm EF and elbow extension (EE) force before and after training using a computerized force transducer and electromyography (EMG) to detect changes in relative antagonist muscle EMG and co-contraction. TMS was used to test the excitability of corticospinal pathways, intra-cortical and inter-hemispheric connections. Central motor drive was assessed using EEG. Results. Before training, there were significant age-related effects. Elderly had significantly (p=0.044) lower strength, antagonist muscle EMG (p=0.001), inter-hemispheric inhibition (IHI; p=0.032), stronger brain activation in beta (13-30 Hz; p<0.001) and gamma band (30-100 Hz; p<0.001) and weakened brain-to-muscle coupling at Cz and C4 compared to young. Both training paradigm equally improved left (open full item for complete abstract)

Committee: Guang Yue PhD (Committee Chair); Sean Veney PhD (Committee Member); Angela Ridgel PhD (Committee Member); Heather Caldwell PhD (Committee Member); David Riccio PhD (Committee Member) Subjects: Aging; Biomedical Engineering; Health Sciences; Neurobiology; Physical Therapy

Doctor of Philosophy, The Ohio State University, 2007, Biomedical Engineering

A multi-paradigm approach integrating three novel computational paradigms: wavelet transforms, chaos theory, and artificial neural networks is developed for EEG-based epilepsy diagnosis and seizure detection. This research challenges the assumption that the EEG represents the dynamics of the entire brain as a unified system. It is postulated that the sub-bands yield more accurate information about constituent neuronal activities underlying the EEG. Consequently, certain changes in EEGs not evident in the original full-spectrum EEG may be amplified when each sub-band is analyzed separately. A novel wavelet-chaos methodology is presented for analysis of EEGs and delta, theta, alpha, beta, and gamma sub-bands of EEGs for detection of seizure and epilepsy. The methodology is applied to three different groups of EEGs: healthy subjects, epileptic subjects during a seizure-free interval (interictal), and epileptic subjects during a seizure (ictal). Two potential markers of abnormality quantifying the non-linear chaotic EEG dynamics are discovered: the correlation dimension and largest Lyapunov exponent. A novel wavelet-chaos-neural network methodology is developed for EEG classification. Along with the aforementioned two parameters, the standard deviation (quantifying the signal variance) is employed for EEG representation. It was discovered that a particular mixed-band feature space consisting of nine parameters and LMBPNN result in the highest classification accuracy (96.7%). To increase the robustness of classification, a novel principal component analysis-enhanced cosine radial basis function neural network classifier is developed. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network employed in the second stage significantly. The new classifier is as accurate as LMBPNN and is twice as robust. Next, biologically realistic artificial neural networks are dev (open full item for complete abstract)

Committee: Hojjat Adeli (Advisor) Subjects:

Master of Science in Biomedical Engineering, Cleveland State University, 2024, Washkewicz College of Engineering

Persistent motor impairment is one of the challenging issues during post-stroke rehabilitation efforts. To augment traditional rehabilitation, a recent clinical trial combined rehabilitation with deep brain stimulation of the cerebellar dentate (DN DBS) and found a remarkable improvement in motor function in stroke patients with upper extremity hemiparesis. Here, we aim to understand whether and how DN DBS modulated motor behavior and the underlying electrophysiology in moderate-to-severe stroke participants who underwent DN DBS therapy. We specifically investigated motor response preparation and reaction time (RT). In this study, the invasive local field potential recordings from the Dentate Nucleus (DN) and scalp EEG were collected before and after DN DBS therapy while participants performed motor RT tasks. The first task was a simple RT (prep) task, where a preparatory cue provided time for response preparation, whereas the second task was a choice RT (go) task where the preparatory cue was omitted. Hypothesis 1 was to investigate if DN DBS altered response preparation in terms of electrophysiology. Hypothesis-2 was to investigate if prep and go tasks differed in terms of behavior and electrophysiology. Hypothesis 3 was to investigate if DN DBS altered RT in prep and go tasks, in terms of behavior and electrophysiology. The electrophysiological metrics investigated were 1) event-related power changes from theta through beta bands, also referred to as event-related desynchronization and synchronization, which provide evidence about the excitability of the ipsilesional cortex and DN (increased desynchronization points to increased excitability and hence increased involvement), 2) cortico-cerebellar connectivity (CCC) in terms of coherence, a measure of phase synchrony between the ipsilesional cortex and DN (increased connectivity points to increased communication between the areas). The behavioral metrics investigated were RT and velocity of movement. Overall, ou (open full item for complete abstract)

Committee: Raghavan Gopalakrishnan (Advisor); Nolan Holland (Committee Member); Andrew B Slifkin (Committee Member); Eric Shearer (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy (PhD), Ohio University, 2024, Clinical Psychology (Arts and Sciences)

Transcranial alternating current stimulation (tACS) is non-invasive brain stimulation technique optimal for targeting specific neural frequencies associated with various aspects of cognition and behavior. Emerging evidence from clinical and experimental studies suggests that tACS applied at alpha range (α-tACS) may reduce pain experience in the context of elevated anxiety or uncertainty. The current study employed a randomized crossover double-blinded design to deliver α-tACS and sham stimulation to 28 participants (13 healthy, 15 with chronic low back pain) while they received mechanical pressure and muscular ischemia pain stimulation. Mechanical pain ratings were found to be higher during tRNS compared to α-tACS. Conversely, ischemic pain ratings were found to be higher during α-tACS compared to tRNS. Further, individuals high in trait anxiety (intolerance of uncertainty) evidenced significantly lower ischemic pain report following α-tACS (M = 35.6, SD = 30.6) compared to sham (M = 46.9, SD = 23.9), t(26) = -3.17, p < 0.001. There were no significant effects of interest found for low back pain ratings among the back pain subsample (assessed at baseline, post-stimulation, and 24-hour follow-up). Pre-post stimulation EEG indicated limited support for higher alpha and broad support for higher theta activity following α-tACS compared to sham. Additionally, increases in alpha and theta at certain sites were associated with lower mechanical pain and low back pain report, respectively. Future research in more representative clinical pain samples is needed to validate and extend this emerging body of research.

Committee: Christopher France (Committee Chair) Subjects: Behavioral Sciences; Clinical Psychology; Medicine

PhD, University of Cincinnati, 2024, Medicine: Neuroscience/Medical Science Scholars Interdisciplinary

Epilepsy affects over 50 million patients worldwide, and one-third of those patients are resistant to current therapeutic options. The development of acquired epilepsy typically begins with a brain insult such as status epilepticus (SE), traumatic brain injury, genetic mutation, or infection. Following such insults, patients enter what is known as the “latent period” of the disease. During the latent period, the brain is changing but patients do not exhibit spontaneous recurrent seizures (SRSs). The latent period ends when a patient first experiences an SRS, at which point the disease has progressed to epilepsy. Epileptogenesis is the process by which a healthy brain becomes prone to SRSs, and it occurs during initial insult as well as throughout the latent period and chronic disease. While the majority of available treatment options aim to reduce SRSs in patients with chronic epilepsy, research has increasingly sought to define and inhibit epileptogenic changes. Successful anti-epileptogenic intervention has the potential to prevent or delay the onset of chronic epilepsy following brain insult and/or prevent continued brain alterations in patients who have already developed chronic epilepsy. The work in this thesis aimed to clarify the molecular mechanisms underlying epileptogenesis. Chapters 2 and 3 discuss the role of microRNAs (miRNAs) in epileptogenesis, and Chapter 4 discusses the role of Ras-MAPK signaling in epileptogenesis. MiRNAs are short, non-coding RNA sequences that regulate post-transcriptional gene expression via translational suppression or degradation of target messenger RNAs (mRNAs). Although many miRNAs have been implicated in epilepsy development, this thesis primarily discusses the role of miR-324-5p. Previous work in our lab showed an anti-convulsant and anti-epileptic effect of miR-324-5p inhibition when administered before brain insult or in chronic epilepsy. Using the intrahippocampal kainic acid model in mice, we tested (open full item for complete abstract)

Committee: Mark Baccei Ph.D. (Committee Chair); Christina Gross Ph.D. (Committee Member); Katrina Peariso M.D. Ph (Committee Member); Anil Jegga DVM MRe (Committee Member); Steve Danzer Ph.D. (Committee Member) Subjects: Neurology

MS, University of Cincinnati, 2022, Engineering and Applied Science: Computer Science

In recent times, the field of EEG-BCI has seen tremendous advancements in terms of research. On the other hand, Deep Learning has been pushing boundaries of what is possible in various domains like Natural Language and Vision. It has helped us to remove the roadblocks of domain expertise and feature engineering, and opened doors to end-to-end learning (from raw data to downstream task). The field of EEG-BCI has recently utilized Deep Learning to decode EEG and extract intent, mostly in the Motor-Imagery paradigm. But most of the current research in this field either have a spatial approach or a temporal approach towards decoding EEG. In this study, we explore the idea of decoding EEG signals in a Spatio- Temporal manner. We take an approach akin to how humans would view and decode EEG readings - by treating the signal as a 2D image matrix. Utilizing the powers of convolutional network to capture local spatial features and transformers to capture global and long term temporal dependencies, we propose an architecture which effectively combines the strength of these two networks to present an end-to-end spatio-temporal architecture capable of decoding Motor-Imagery intent from raw EEG signals.

Committee: Anca Ralescu Ph.D. (Committee Member); Dan Ralescu Ph.D. (Committee Member); Kenneth Berman Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy, University of Toledo, 2022, Exercise Science

Joint injury is the most common cause of pain and disability in young adults, contributing to physical in-activity and disenfranchisement with exercise. One such injury, anterior cruciate ligament injury and surgical reconstruction (ACLR), alters neural afferent activity originating from the periarticular tissue. Since movement is dependent upon sensory input, sensory and processing dysfunctions contribute to changes in movement capability, such as thigh muscle weakness and balance impairments. Although subtle, these cascade into withdrawal from physical activity and contribute to joint degeneration (knee osteoarthritis). Therapies, such as strength training in physical rehabilitation, often do not consider whether augmenting sensory input can improve movement. Fortunately, novel applications of widely available modalities, such as visual biofeedback, electrical stimulation (TENS), and goal-oriented attention show preliminary alignment with modifiable neural impairments following ACL injury. In manuscript 1, we compare brain activity between individuals with ACLR and uninjured controls during single-limb balance and determine the influence of neuromodulatory interventions (External focus of attention [EF] and TENS) on cortical activity and balance performance. Our results demonstrate that (1) individuals with ACLR exhibit lower somatosensory processing and greater motor inhibition compared to controls and (2) visual biofeedback resulted in favorable reductions in motor-planning and increases in somatosensory and motor activity. In manuscript 2, we compare cortical motor planning activity and response selection performance between individuals with ACLR and uninjured controls during a reaction time and response selection task. Here, the ACLR group demonstrated (1) greater motor planning and response inhibition during the task, and (2) more errant performance suggesting poorer decision making in the presence of widespread cortical inhibition. In manuscript 3, we compar (open full item for complete abstract)

Committee: Grant Norte (Advisor); Matt Stock (Committee Member); Jochen Baumeister (Committee Member); Amanda Murray (Committee Member); David Bazett-Jones (Committee Member) Subjects: Health; Medicine; Neurosciences; Physical Therapy; Rehabilitation; Sports Medicine

Master of Science (M.S.), University of Dayton, 2021, Electrical Engineering

Humans have used tones in religion, community gatherings, meditation, and other spiritual practices for millennia. In Buddhism, tones such as the Ohm are seen as deeply religious. In Islam, the call to prayer is recited five times per day, often in repetitive and similar tones, to call the Muslim community for prayer. In Catholicism, many songs utilize the same chords to praise God. In the US, the national anthem and pledge of allegiance are connected with national pride. In many tribal religions, repetitive drumbeats are used to inspire trances and hold rituals. More recently, binaural tones have increased in popularity. Many people have started using binaural tones to relax, sleep, or concentrate. However, few research studies have examined the effect of binaural tones on electroencephalogram (EEG) waveforms. In this thesis, the effect of several binaural tones on EEG waveforms is analyzed through a human subjects research study in which participants performed computational tasks of varying cognitive load. Neural activity as a function of peak amplitude was recorded using an Emotiv EPOC electrode array. The results indicate that while vast differences exist amongst individuals, there does appear to be an increase in neural activity after repeated short exposure to binaural tones at 15 Hz, when averaged across all test subjects. In addition, subjects on average demonstrated improvement in both speed (13.73%) and accuracy (40%) for mathematical calculations of varying complexity while listening to binaural tones when compared to calculations of similar complexity that were performed with no auditory stimulus.

Committee: Amy Neidhard-Doll (Advisor); Vamsy Chodavarapu (Committee Member); Vijayan Asari (Committee Member) Subjects: Bioinformatics; Biomedical Engineering; Biomedical Research; Electrical Engineering

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2021, Psychology/Experimental

Howard Casey Cromwell, Advisor Incentive contrast effects occur when reward values change from previous experience and comparisons are made between the previous and current value. The value reupdating depends on the relative outcomes that are comparable. The changes occur after exposure to the identical reward whose value has changed depending upon the differences between previous and current experiences. Studies have been done to examine the incentive contrast effects using extrinsic rewards, such as money and points earned. However, no studies have been done to investigate if intrinsic rewards can induce incentive contrast effects in humans and in general, how brain oscillations change related to incentive contrast effects. We predicted that the order of exposure to different levels of difficulty of the game would lead to incentive contrast effects. Specifically, we hypothesized that the performance of game playing would be enhanced in the easier-level immediately preceded by the difficult-level (positive contrast) and impaired in the difficult-level when preceded by an easier-level (negative contrast). Moreover, the greater positive emotional response was predicted to lead to better performance, while negative emotion would result in bad performance. In Experiment 2, brain oscillations were recorded at Fz and Cz sites after each session of game playing. We hypothesized that the power of beta oscillation would increase in positive incentive contrast, while the power of theta oscillation would increase in negative incentive contrast. Furthermore, we believe the theta/beta ratio would decrease in positive incentive contrast and increase in negative incentive contrast. Results supported these predictions partially that only negative behavioral incentive contrast effect was revealed in Experiment 1. The performance in the games was positively related to motivation and positive affect and negatively related to negative affect (frustration). No incentive contrast effe (open full item for complete abstract)

Committee: Howard Cromwell Ph.D (Advisor); Khani Begum Ph.D (Other); William O’Brien Ph.D (Committee Member); Sherona Garrett-Ruffin Ph.D (Committee Member) Subjects: Psychology

Master of Computing and Information Systems, Youngstown State University, 2021, Department of Computer Science and Information Systems

At present, most of the leading automobile manufacturers and people who work in the academic field conduct studies about self-driving cars. Self-driving capabilities have improved in automobiles, and the potential benefits and dangers of this innovation for individuals and the environment are deliberated broadly. One potentially dangerous situation that has been studied in detail is related to the process of take-overs when autonomous vehicles, specifically the ones at level 3, fail. Most of the hazards caused during these take-overs can be attributed to a variety of factors, which can be classified as environmental factors, vehicle factors, and human factors. Lately, human factors have stood out as an area of study to improve the safety performance of level 3 autonomous vehicles. Some of the most important examples of human factors are the driver's distraction and emotional states during the take-over process of an autonomous vehicle, both of which have great potential in reducing the “driver's" driving skills and leading to fatal accidents. Most of the autonomous vehicles on the market, are at level 3, which the drivers have to take over the control of the vehicle in some road scenarios when the vehicle fails unexpectedly. When the autonomous vehicle fails, the "driver" is provided with a short time span, which will be referred to as buffer-time in this study, before s/he takes the control of the vehicle. Many scholars investigated the optimum buffer-time that will have the most positive effect on the driver's take-over performance, but they have not reached an agreement. However, an early buffer-time of 8 seconds and a late buffer-time of 4 seconds have been utilized in various prior studies. Because of this reason, it is important to understand the effects of early and late buffer-times (4 and 8 seconds) on driver's emotional states. This study investigates the effect of buffer-time (4 and 8 seconds) on the driver's emotional states. 20 young drivers participate (open full item for complete abstract)

Committee: Abdu Arslanyilmaz PhD (Advisor); Yong Zhang PhD (Committee Member); Alina Lazar PhD (Committee Member) Subjects: Computer Engineering; Computer Science; Statistics

MS, University of Cincinnati, 2020, Medicine: Clinical and Translational Research

Objective: Seizures and abnormal periodic or rhythmic patterns are observed on continuous electroencephalography (cEEG) in up to half of patients hospitalized with moderate-to-severe traumatic brain injury (TBI). We aimed to determine the impact of seizures and abnormal periodic or rhythmic patterns on cognitive outcome 3 months following moderate-to-severe TBI. Design: Post-hoc analysis of a multicenter randomized, controlled phase 2 clinical trial conducted from 2010-2016 across 20 US Level I trauma centers. Patients with non-penetrating TBI and post-resuscitation Glasgow Coma Scale (GCS) 4–12 were included. Bedside cEEG was initiated per protocol upon admission to intensive care and the burden of ictal-interictal continuum (IIC) patterns including seizures was quantified. A summary global cognition score at 3 months following injury was used as the primary outcome. Measures and Main Results: 142 patients (age mean+/-SD 32+/-13 years; 131 [92%] male) survived with a mean global cognition score of 81+/-15; nearly one-third were considered to have poor functional outcome. 89/142 (63%) patients underwent cEEG, of whom 13/89 (15%) had severe IIC patterns. The quantitative burden of IIC patterns correlated inversely with the global cognition score (r=-0.57; p=0.04). In multiple variable analysis, the burden of IIC patterns was independently associated with the global cognition score after controlling for demographics, pre-morbid estimated intelligence, injury severity, sedatives and antiseizure drugs. Conclusions: The burden of seizures and abnormal periodic or rhythmic patterns was independently associated with worse cognition at 3 months following TBI. Their impact on longer-term cognitive endpoints and the potential benefits of seizure detection and treatment in this population warrant prospective study.

Committee: Scott Langevin Ph.D. (Committee Chair); Daniel Woo M.D. (Committee Member); Nanhua Zhang Ph.D. (Committee Member) Subjects: Surgery

Master of Science, The Ohio State University, 2020, Psychology

Cognitive neuroscientists use a variety of methodologies to answer difficult questions about cognitive processes. Each of these methodologies have distinctive strengths and limitations. In order to develop a comprehensive theory of a cognitive process, often disjointed findings must be integrated. Joint modeling (Palestro et al., 2018) is a framework with which these distinct modalities can be mathematically linked. Here, we propose a framework to formally specify neurally-based theories via a type of joint model called an integrative model. We use response inhibition, measured via the stop-signal task (Logan, Cowan, & Davis, 1984), as a case study, because of the extensive literature across a variety of behavioral and neural domains. Our goal is to specify a formal theory of the neural dynamics of stopping using an integrative model to test the ability of this model to reproduce major findings in the stop-signal literature. First, we provide a review of the stop-signal literature and explain how these findings inform our model. Second, we introduce two open-access stop-signal datasets, including an fMRI study (Aron & Poldrack, 2006) and an EEG study (Castiglione, Wagner, Anderson, & Aron, 2019), which provide the stimuli for our simulations. Additionally, we use the neural data to define our regions of interest. Third, we specify our integrative model and describe how a single dynamical latent state model can be used to simultaneously predict both EEG and fMRI data. Fourth, we present results from simulation studies showing the dynamics of the underlying state space, and compare the simulated results using the stimuli from both experiments to expected findings based on the literature and the observed findings of the specific experiments. We find that the simulated models are capable of capturing multiple neural signatures of successful stopping, particularly in the inferior frontal gyrus. We conclude by discussing how this framework could be improved by allowing fu (open full item for complete abstract)

Committee: Zeynep Saygin (Advisor); David Osher (Advisor); Andrew Leber (Committee Member) Subjects: Cognitive Psychology; Neurosciences

Master of Science in Biomedical Engineering (MSBME), Wright State University, 2020, Biomedical Engineering

High cognitive workload occurs when excessive working memory resources have been deployed to resolve sensory and cognitive processing, resulting in decremented task performance. The P300 event-related potential (ERP) component has shown sensitivity to cognitive load, and it was hypothesized that an attenuated P300 amplitude could be indicative of high cognitive load. We tested this hypothesis by having eight participants complete two continual performance tasks at increasing workload levels while simultaneously performing an oddball task, evoking P300 ERPs in either the auditory or tactile sensory channel. In our experiment, electroencephalographic recordings were collected over the parietal region to observe the P300 component. Our results show a downward trend in P300 amplitude as workload increased when performing auditory oddball tasks, although P300's elicited by the tactile oddball tasks produced no consistent trend. These results suggest cognitive load indexing is possible in select sensory channels, though additional investigation is required.

Committee: Sherif Elbasiouny Ph.D. (Advisor); Ulas Sunar Ph.D. (Committee Member); Matthew Sherwood Ph.D. (Committee Member) Subjects: Biomedical Engineering; Neurosciences

Master of Sciences, Case Western Reserve University, 0, EECS - Computer and Information Sciences

Brain functional connectivity measures are used to study interactions between brain regions in various neurological disorders such as Alzheimer's Disease and epilepsy. In particular, high-resolution electrophysiological signal data recorded from intracranial electrodes, such as stereotactic electroencephalography (SEEG) signal data, is often used to characterize the properties of brain connectivity in neurological disorders. For example, SEEG data is used to lateralize the epileptogenic zone and characterize seizure networks in epilepsy. However, there are several computational challenges associated with efficient and scalable analysis of signal data in neurological disorders due to the large volume and complexity of signal data. In order to address the challenges associated with processing and analyzing signal datasets, we have developed an integrated platform called Neuro-Integrative Connectivity (NIC) platform that integrates and streamlines multiple data processing and analysis steps into a single tool. In particular, in this thesis we have developed a suite of new approaches covering signal data format, indexing structure, and Apache Spark libraries to support efficient and scalable signal data management for applications in neurological disorders such as epilepsy. Our evaluations demonstrate the utility of Apache Spark in supporting neuroscience Big Data application; however, our results also demonstrate that Apache Spark is not well suited for all types of computational tasks associated with signal data management. Therefore, the overall objective of this thesis is to identify specific computational tasks that benefit from the use of main memory-based Apache Spark methods in neuroscience Big Data applications. The new NIC platform developed in this thesis is a significant resource for the brain connectivity research community as it has applications in real world settings for advancing research in neurological disorders using signal data.

Committee: Satya Sahoo (Advisor); Jing Li (Committee Chair); An Wang (Committee Member) Subjects: Bioinformatics; Computer Science

Master of Science in Biomedical Engineering (MSBME), Wright State University, 2020, Biomedical Engineering

The posterior alpha rhythm, seen in human electroencephalograms (EEG), is posited to originate from cycling inhibitory/excitatory states of visual relay cells in the thalamus, which could result in discrete sampling of visual information. Here, we tested this hypothesis by presenting light flashes at perceptual threshold intensity through closed eyelids to 20 participants during times of spontaneous alpha oscillations. Alpha phase and amplitude were calculated relative to each individual's retina-to-V1 conduction delay, estimated by the individuals' C1 visual-evoked potential latency. Our results show that an additional 20.96% of stimuli are observed when afferenting at V1 during an alpha wave trough (272.41°) than at peak (92.41°) phase. Additionally, the perception-phase relationship is observed at high, but not low alpha amplitudes. These results support the visual sampling hypothesis and, considering the alpha rhythm's negative correlation with attention, suggests that the alpha rhythm facilitates attention by down-sampling task-irrelevant information.

Committee: Sherif Elbasiouny Ph.D. (Advisor); Subhashini Ganapathy Ph.D. (Committee Member); Assaf Harel Ph.D. (Committee Member) Subjects: Biomedical Engineering; Neurosciences; Psychology

Master of Science (M.S.), University of Dayton, 2020, Biology

Calcium (Ca2+) ions comprise critical second messengers for a wide variety of cellular processes, including gene expression, cell proliferation and death, and metabolism; neurons are no exception to this. Intraneuronal Ca2+ handling regulates processes such as long-term potentiation (LTP), synaptic transmission, and generation of firing patterns. Subsequently, disruptions of neuronal Ca2+ has been implicated in several neuropsychiatric and neurodegenerative disorders. A key component of the neuronal Ca2+ handling toolkit is the sarco/endoplasmic reticulum Ca2+ ATPase (SERCA) pump, specifically the SERCA2 isoform. We have recently identified a novel SERCA regulator to be expressed in the central nervous system. In the context of the current thesis, we assessed how loss of this gene may affect the neurochemical status and the cortical oscillatory activity upon genetic ablation of this gene in mice. Specifically, in the first part of the study an ex vivo neurochemical screening in distinct brain regions was conducted using high performance liquid chromatography (HPLC) with coulometric detection focusing on monoaminergic (i.e., noradrenaline, serotonin, dopamine) and aminoacidergic neurotransmission (i.e., glutamate, aspartate, γ-aminobutyric acid); in the second part of the study an electroencephalogram (EEG)-based power spectral analysis was conducted in order to assess how loss of this molecular player affects cortical oscillations in the different vigilance states in mice lacking this gene and in their wild type controls. Overall, current findings show that ablation of this gene results in sex-dependent and brain region-specific endophenotypic alterations and further provide valuable insights to understanding the role of this novel player in brain physiology and pathophysiology.

Committee: Pothitos Pitychoutis Ph.D. (Advisor); Karolyn Hansen Ph.D. (Committee Member); Amit Singh Ph.D. (Committee Member) Subjects: Biology; Neurobiology; Neurosciences

Master of Science (MS), Ohio University, 2020, Clinical Psychology (Arts and Sciences)

Research suggests that anxiety can be conceptualized as a two-dimensional construct comprising anxious apprehension (worry, negative expectations, fears, and rumination about the future) and anxious arousal (hyperarousal related to current events, accompanied by panic-like symptoms such as heart pounding, dizziness, shortness of breath, and sweating). A potential biomarker of these dimensions of anxiety is asymmetrical brain activity based on the alpha EEG band. Prior research shows that anxious apprehension is associated with left frontal asymmetry and anxious arousal is associated with right frontal asymmetry. However, there are limitations of prior studies such as using an extreme group approach, conflating state and trait variability, and using a cross-sectional design. In the present study, ecological momentary assessment (EMA) was used to separate state and trait variability of anxious apprehension and anxious arousal in community members and graduate students. Contrary to prediction, there were no significant relations between baseline frontal asymmetry with baseline or EMA-collected trait anxious apprehension and anxious arousal, controlling for state levels of these constructs. However, baseline depression, a control variable in this study, was associated with RFA. Findings suggest that frontal asymmetry may be a more robust indicator of depression than of anxious apprehension or anxious arousal during the resting state task. Future research is needed to test the association of asymmetry with anxious apprehension and anxious arousal during experimental manipulation of anxious apprehension and anxious arousal.

Committee: Nicholas Allan (Advisor); Julie Suhr (Committee Member); Dominik Mischkowski (Committee Member) Subjects: Clinical Psychology; Physiological Psychology; Psychology

Master of Science (MS), Wright State University, 2020, Physiology and Neuroscience

During scene perception, studies have shown the importance of the global distribution of a scene. Electrophysiological studies have found these global effects concentrated corresponding to the second positive and first negative peaks (P2 and N1, respectively) of the Event-related potential (ERP) during the first 600 ms of scene perception. We sought to understand in Experiment 1, to what extent early responses to scenes were driven by mid-level global information such as the degree of naturalness or openness in a scene image in the absence of specific low-and high-level information (color and semantic object detail). This was done using artificially-generated stimuli controlling for two global scene properties (GSPs) of spatial boundary and naturalness while minimizing color and semantic object information. Significant effects were observed on the P2 and N1 components as well as the P1 component. However, the question of whether scene perception is dominated by global or local factors had yet to be answered leading to Experiment 2. During Experiment 2, for half the trials scenes were presented in an inverted orientation. We found only an orientation interaction approaching significance corresponding to the P1 time course.

Committee: Assaf Harel Ph.D. (Advisor); Sherif M. Elbasiouny Ph.D. (Committee Member); Joe Houpt Ph.D. (Committee Member) Subjects: Physiological Psychology

Master of Science, Miami University, 2019, Computational Science and Engineering

One of the most significant problems facing individuals in the modern workplace is injuries due to physical exertion. The purpose of this study is to demonstrate a correlation with elevations of Electroencephalography (EEG) biological signals alongside Electrocardiography (ECG or EKG) and oxygen levels to determine the thresholds of exertion for individuals. Demonstrating such a correlation between these measurements would provide a basis for future investigations into developing technologies to monitor individuals in physically intense work areas and recovery through physical therapy. This goal was achieved in this thesis using a nonintrusive EEG headset sensor with a compatible ECG lead. This study also introduces the use of various signal processing and data comparison techniques to demonstrate correlations between the various biological signals. A secondary portion of this research utilizes a newer signal processing technique adopted from a previous work.

Committee: Qihou Zhou Dr. (Committee Chair); AKM Jahangir Majumder Dr. (Committee Member); Donald Ucci Dr. (Committee Member); Miao Wang Dr. (Committee Member) Subjects: Biomedical Engineering; Electrical Engineering