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Articles 1 - 7 of 7
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Improved Motor Imagery Decoding Using Deep Learning Techniques, Olawunmi Olaboopo George
Improved Motor Imagery Decoding Using Deep Learning Techniques, Olawunmi Olaboopo George
Dissertations (1934 -)
Motor imagery (MI) has been one of the most used paradigms for building brain-computer interfaces (BCI), widely used in neurorehabilitation, for restoring functionality to damaged parts of a neurologically deficient person. The existing motor imagery techniques have largely employed feature extraction techniques such as the power spectral density (PSD) and the common spatial patterns (CSP) before classification, using traditional machine learning algorithms such as support vector machines (SVM) and linear discriminant analysis (LDA). These algorithms are quite limited in their ability to generate feature representations for certain types of signals, limiting the potential for improvements in the decoding process. Also, …
Eeg Characterization Of Sensorimotor Networks: Implications In Stroke, Dylan Blake Snyder
Eeg Characterization Of Sensorimotor Networks: Implications In Stroke, Dylan Blake Snyder
Dissertations (1934 -)
The purpose of this dissertation was to use electroencephalography (EEG) to characterize sensorimotor networks and examine the effects of stroke on sensorimotor networks. Sensorimotor networks play an essential role in completion of everyday tasks, and when damaged, as in stroke survivors, the successful completion of seemingly simple motor tasks becomes fantasy. When sensorimotor networks are impaired as a result of stroke, varying degrees of sensorimotor deficits emerge, most often including loss of sensation and difficulty generating upper extremity movements. Although sensory therapies, such as the application of tendon vibration, have been shown to reduce the sensorimotor deficits after stroke, the …
Characterization Of Neuroimage Coupling Between Eeg And Fmri Using Within-Subject Joint Independent Component Analysis, Nicholas Heugel
Characterization Of Neuroimage Coupling Between Eeg And Fmri Using Within-Subject Joint Independent Component Analysis, Nicholas Heugel
Dissertations (1934 -)
The purpose of this dissertation was to apply joint independent component analysis (jICA) to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to characterize the neuroimage coupling between the two modalities. EEG and fMRI are complimentary imaging techniques which have been used in conjunction to investigate neural activity. Understanding how these two imaging modalities relate to each other not only enables better multimodal analysis, but also has clinical implications as well. In particular, Alzheimer’s, Parkinson’s, hypertension, and ischemic stroke are all known to impact the cerebral blood flow, and by extension alter the relationship between EEG and fMRI. By characterizing …
Examining The Durability Of Peers For Adolescents With Asd: Maintenance Of Neurological And Behavioral Effects, Bridget Kathleen Dolan
Examining The Durability Of Peers For Adolescents With Asd: Maintenance Of Neurological And Behavioral Effects, Bridget Kathleen Dolan
Dissertations (1934 -)
To date, there are no known published studies that have assessed the maintenance of treatment effects in the context of neurological changes and their relationship to behavioral outcomes following a social skills intervention for adolescents with Autism Spectrum Disorder (ASD). The few studies that have incorporated long-term assessment into their design have focused exclusively on sustained behavioral responses to treatment. Individuals with ASD across the lifespan exhibit aberrant neural activity, which is thought to underlie social skill deficits noted in persons on the spectrum. Thus, this study sought to examine the impact of a social skills intervention, the Program for …
Reinforcement Learning, Error-Related Negativity, And Genetic Risk For Alzheimer's Disease, Christina Marie Figueroa
Reinforcement Learning, Error-Related Negativity, And Genetic Risk For Alzheimer's Disease, Christina Marie Figueroa
Dissertations (1934 -)
Reinforcement learning (RL) has been widely used as a model of animal and human learning and decision-making. The neural networks underlying RL involve many of the same structures primarily affected by Alzheimer’s disease (AD) such as the hippocampus. Yet, RL and non-invasive evaluation of its neural underpinnings have been underutilized as a framework for understanding disease pathology and its pre-clinical states. This study aimed to provide a novel approach for assessing subtle changes in asymptomatic apolipoprotein-E (APOE) carriers and non-carriers. Electroencephalography was collected from forty APOE genotyped older adults (Male n = 11; Mage = 79.30; Meducation = 14.88 years) …
Neural Plasticity In Response To Intervention In Adolescents With Autism Spectrum Disorders, Sheryl Jayne Stevens
Neural Plasticity In Response To Intervention In Adolescents With Autism Spectrum Disorders, Sheryl Jayne Stevens
Dissertations (1934 -)
Current theories of Autism Spectrum Disorders (ASD) suggest that they may develop from the transactional interaction between biological risk factors and environmental processes (Dawson et al., 2009). Due to the brain’s experience-expectant nature, one’s degree of social exposure may have a significant impact on their brain development and behavioral presentation. In addition to the primary critical neurodevelopmental period identified in early childhood, recent research has demonstrated a second period of substantial neurodevelopment during the adolescent period (Sisk & Foster, 2004). This study investigated the neural and behavioral impact of participation in an empirically validated behavioral intervention (The Program for the …
Integration Of Eeg-Fmri In An Auditory Oddball Paradigm Using Joint Independent Component Analysis, Jain Mangalathu Arumana
Integration Of Eeg-Fmri In An Auditory Oddball Paradigm Using Joint Independent Component Analysis, Jain Mangalathu Arumana
Dissertations (1934 -)
The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. The overall objective of this dissertation is to determine the sensitivity and limitations of joint independent component analysis (jICA) within-subject for integration of ERP and fMRI data collected simultaneously in a parametric auditory oddball paradigm. The main experimental finding in this work is that jICA revealed significantly stronger and more extensive activity in brain regions associated with the auditory P300 ERP than a P300 linear regression analysis, both at the group level and within-subject. The results …