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- Creators: School of Mathematical and Statistical Sciences
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
Brain-computer interface technology establishes communication between the brain and a computer, allowing users to control devices, machines, or virtual objects using their thoughts. This study investigates optimal conditions to facilitate learning to operate this interface. It compares two biofeedback methods, which dictate the relationship between brain activity and the movement of a virtual ball in a target-hitting task. Preliminary results indicate that a method in which the position of the virtual object directly relates to the amplitude of brain signals is most conducive to success. In addition, this research explores learning in the context of neural signals during training with a BCI task. Specifically, it investigates whether subjects can adapt to parameters of the interface without guidance. This experiment prompts subjects to modulate brain signals spectrally, spatially, and temporally, as well differentially to discriminate between two different targets. However, subjects are not given knowledge regarding these desired changes, nor are they given instruction on how to move the virtual ball. Preliminary analysis of signal trends suggests that some successful participants are able to adapt brain wave activity in certain pre-specified locations and frequency bands over time in order to achieve control. Future studies will further explore these phenomena, and future BCI projects will be advised by these methods, which will give insight into the creation of more intuitive and reliable BCI technology.
ContributorsLancaster, Jenessa Mae (Co-author) / Appavu, Brian (Co-author) / Wahnoun, Remy (Co-author, Committee member) / Helms Tillery, Stephen (Thesis director) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / Department of Psychology (Contributor)
Created2014-05
Description
This project is a small scale investigation of various factors concerning "Flow" in Piano Performance. "Flow" is the sweet spot where ability and challenge are about equal, and usually high (Csikszentmihalyi 1990). Piano performance is a state of playing the piano with some intent to perform. In this case, the intent is to create something new or improvise. Improvisation is one form of expressive creativity on the piano stemming from some knowledge and extrapolation upon that knowledge (Nachmanovitch 82). Creativity is essential to the development of new music, and though extensive literature exists on both creativity and music independently, there is a gap in research regarding links between the two (Macdonald et al. 2006). This project aims to address some of these gaps by working with piano players and non-musicians of various technical skill levels to examine the "Flow" state in improvisation as well as potential factors affecting creative performance. Factors such as listening, self-confidence, frustration in methodology, and meditation practices were found to correlate positively with technical skill. Participants who completed the practice program were able to reconstruct challenges and enter the "Flow" state in improvisation regardless of high or low technical scores.
ContributorsDorr, Alexander Nathan (Author) / Kaplan, Robert (Thesis director) / Parker, John (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Motor learning is the process of improving task execution according to some measure of performance. This can be divided into skill learning, a model-free process, and adaptation, a model-based process. Prior studies have indicated that adaptation results from two complementary learning systems with parallel organization. This report attempted to answer the question of whether a similar interaction leads to savings, a model-free process that is described as faster relearning when experiencing something familiar. This was tested in a two-week reaching task conducted on a robotic arm capable of perturbing movements. The task was designed so that the two sessions differed in their history of errors. By measuring the change in the learning rate, the savings was determined at various points. The results showed that the history of errors successfully modulated savings. Thus, this supports the notion that the two complementary systems interact to develop savings. Additionally, this report was part of a larger study that will explore the organizational structure of the complementary systems as well as the neural basis of this motor learning.
ContributorsRuta, Michael (Author) / Santello, Marco (Thesis director) / Blais, Chris (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / School of Human Evolution & Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Older adults tend to learn at a lesser extent and slower rate than younger individuals. This is especially problematic for older adults at risk to injury or neurological disease who require therapy to learn and relearn motor skills. There is evidence that the reticulospinal system is critical to motor learning and that deficits in the reticulospinal system may be responsible, at least in part, for learning deficits in older adults. Specifically, delays in the reticulospinal system (measured via the startle reflex) are related to poor motor learning and retention in older adults. However, the mechanism underlying these delays in the reticulospinal system is currently unknown.
Along with aging, sleep deprivation is correlated with learning deficits. Research has shown that a lack of sleep negatively impacts motor skill learning and consolidation. Since there is a link between sleep and learning, as well as learning and the reticulospinal system, these observations raise the question: does sleep deprivation underlie reticulospinal delays? We hypothesized that sleep deprivation was correlated to a slower startle response, indicating a delayed reticulospinal system. Our objectives were to observe the impact of sleep deprivation on 1) the startle response (characterized by muscle onset latency and percentage of startle responses elicited) and 2) functional performance (to determine whether subjects were sufficiently sleep deprived).
21 young adults participated in two experimental sessions: one control session (8-10 hour time in bed opportunity for at least 3 nights prior) and one sleep deprivation session (0 hour time in bed opportunity for one night prior). The same protocol was conducted during each session. First, subjects were randomly exposed to 15 loud, startling acoustic stimuli of 120 dB. Electromyography (EMG) data measured muscle activity from the left and right sternocleidomastoid (LSCM and RSCM), biceps brachii, and triceps brachii. To assess functional performance, cognitive, balance, and motor tests were also administered. The EMG data were analyzed in MATLAB. A generalized linear mixed model was performed on LSCM and RSCM onset latencies. Paired t-tests were performed on the percentage of startle responses elicited and functional performance metrics. A p-value of less than 0.05 indicated significance.
Thirteen out of 21 participants displayed at least one startle response during their control and sleep deprived sessions and were further analyzed. No differences were found in onset latency (RSCM: control = 75.87 ± 21.94ms, sleep deprived = 82.06 ± 27.47ms; LSCM: control = 79.53 ± 17.85ms, sleep deprived = 78.48 ± 20.75ms) and percentage of startle responses elicited (control = 84.10 ± 15.53%; sleep deprived = 83.59 ± 18.58%) between the two sessions. However, significant differences were observed in reaction time, TUG with Dual time, and average balance time with the right leg up. Our data did not support our hypothesis; no significant differences were seen between subjects’ startle responses during the control and sleep deprived sessions. However, sleep deprivation was indicated with declines were observed in functional performance. Therefore, we concluded that sleep deprivation may not affect the startle response and underlie delays in the reticulospinal system.
Along with aging, sleep deprivation is correlated with learning deficits. Research has shown that a lack of sleep negatively impacts motor skill learning and consolidation. Since there is a link between sleep and learning, as well as learning and the reticulospinal system, these observations raise the question: does sleep deprivation underlie reticulospinal delays? We hypothesized that sleep deprivation was correlated to a slower startle response, indicating a delayed reticulospinal system. Our objectives were to observe the impact of sleep deprivation on 1) the startle response (characterized by muscle onset latency and percentage of startle responses elicited) and 2) functional performance (to determine whether subjects were sufficiently sleep deprived).
21 young adults participated in two experimental sessions: one control session (8-10 hour time in bed opportunity for at least 3 nights prior) and one sleep deprivation session (0 hour time in bed opportunity for one night prior). The same protocol was conducted during each session. First, subjects were randomly exposed to 15 loud, startling acoustic stimuli of 120 dB. Electromyography (EMG) data measured muscle activity from the left and right sternocleidomastoid (LSCM and RSCM), biceps brachii, and triceps brachii. To assess functional performance, cognitive, balance, and motor tests were also administered. The EMG data were analyzed in MATLAB. A generalized linear mixed model was performed on LSCM and RSCM onset latencies. Paired t-tests were performed on the percentage of startle responses elicited and functional performance metrics. A p-value of less than 0.05 indicated significance.
Thirteen out of 21 participants displayed at least one startle response during their control and sleep deprived sessions and were further analyzed. No differences were found in onset latency (RSCM: control = 75.87 ± 21.94ms, sleep deprived = 82.06 ± 27.47ms; LSCM: control = 79.53 ± 17.85ms, sleep deprived = 78.48 ± 20.75ms) and percentage of startle responses elicited (control = 84.10 ± 15.53%; sleep deprived = 83.59 ± 18.58%) between the two sessions. However, significant differences were observed in reaction time, TUG with Dual time, and average balance time with the right leg up. Our data did not support our hypothesis; no significant differences were seen between subjects’ startle responses during the control and sleep deprived sessions. However, sleep deprivation was indicated with declines were observed in functional performance. Therefore, we concluded that sleep deprivation may not affect the startle response and underlie delays in the reticulospinal system.
ContributorsGopalakrishnan, Smita (Author) / Honeycutt, Claire (Thesis director) / Petrov, Megan (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05