Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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Description
This research is focused on two separate but related topics. The first uses an electroencephalographic (EEG) brain-computer interface (BCI) to explore the phenomenon of motor learning transfer. The second takes a closer look at the EEG-BCI itself and tests an alternate way of mapping EEG signals into machine commands. We test whether motor learning transfer is more related to use of shared neural structures between imagery and motor execution or to more generalized cognitive factors. Using an EEG-BCI, we train one group of participants to control the movements of a cursor using embodied motor imagery. A second group is trained to control the cursor using abstract motor imagery. A third control group practices moving the cursor using an arm and finger on a touch screen. We hypothesized that if motor learning transfer is related to the use of shared neural structures then the embodied motor imagery group would show more learning transfer than the abstract imaging group. If, on the other hand, motor learning transfer results from more general cognitive processes, then the abstract motor imagery group should also demonstrate motor learning transfer to the manual performance of the same task. Our findings support that motor learning transfer is due to the use of shared neural structures between imaging and motor execution of a task. The abstract group showed no motor learning transfer despite being better at EEG-BCI control than the embodied group. The fact that more participants were able to learn EEG-BCI control using abstract imagery suggests that abstract imagery may be more suitable for EEG-BCIs for some disabilities, while embodied imagery may be more suitable for others. In Part 2, EEG data collected in the above experiment was used to train an artificial neural network (ANN) to map EEG signals to machine commands. We found that our open-source ANN using spectrograms generated from SFFTs is fundamentally different and in some ways superior to Emotiv's proprietary method. Our use of novel combinations of existing technologies along with abstract and embodied imagery facilitates adaptive customization of EEG-BCI control to meet needs of individual users.
Contributorsda Silva, Flavio J. K (Author) / Mcbeath, Michael K (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Presson, Clark (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2013
Description
Adults with autism spectrum disorder (ASD) face heightened risk of co-occurring psychiatric conditions, especially depression and anxiety disorders, which contribute to seven-fold higher suicide rates than the general population. Mindfulness-based stress reduction (MBSR) is an 8-week meditation intervention centered around training continuous redirection of attention toward present moment experience, and has been shown to improve mental health in autistic adults. However, the underlying therapeutic neural mechanisms and whether behavioral and brain changes are mindfulness-specific have yet to be elucidated. In this randomized clinical trial, I utilized functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to characterize fMRI functional activity (Study 1) and connectivity (Study 2) and EEG neurophysiological (Study 3) changes between MBSR and a social support/relaxation education (SE) active control group. Study 1 revealed an MBSR-specific increase in the midcingulate cortex fMRI blood oxygen level dependent signal which was associated with reduced depression. Study 2 identified nonspecific intervention improvements in depression, anxiety, and autistic, and MBSR-specific improvements in the mindfulness trait ‘nonjudgment toward experience’ and in the executive functioning domain of working memory. MBSR-specific decreases in insula-thalamus and frontal pole-posterior cingulate functional connectivity was associated with improvements in anxiety, mindfulness traits, and working memory abilities. Both MBSR and SE groups showed decreased amygdala-sensorimotor and frontal pole-insula connectivity which correlated with reduced depression. Study 3 consisted of an EEG spectral power analysis at high-frequency brainwaves associated with default mode network (DMN) activity. Results showed MBSR-specific and nonspecific decreases in beta- and gamma-band power, with effects being generally more robust in the MBSR group; additionally, MBSR-specific decreases in posterior gamma correlated with anxiolytic effects. Collectively, these studies suggest: 1) social support is sufficient for improvements in depression, anxiety, and autistic traits; 2) MBSR provides additional benefits related to mindfulness traits and working memory; and 3) distinct and shared neural mechanisms of mindfulness training in adults with ASD, implicating the salience and default mode networks and high-frequency neurophysiology. Findings bear relevance to the development of personalized medicine approaches for psychiatric co-morbidity in ASD, provide putative targets for neurostimulation research, and warrant replication and extension using advanced multimodal imaging approaches.
ContributorsPagni, Broc (Author) / Braden, B. Blair (Thesis advisor) / Newbern, Jason (Thesis advisor) / Davis, Mary (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2022