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- Creators: Arizona State University
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
Myoelectric control is lled with potential to signicantly change human-robot interaction.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
ContributorsIson, Mark (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
Description
Adapting to one novel condition of a motor task has been shown to generalize to other naïve conditions (i.e., motor generalization). In contrast, learning one task affects the proficiency of another task that is altogether different (i.e. motor transfer). Much more is known about motor generalization than about motor transfer, despite of decades of behavioral evidence. Moreover, motor generalization is studied as a probe to understanding how movements in any novel situations are affected by previous experiences. Thus, one could assume that mechanisms underlying transfer from trained to untrained tasks may be same as the ones known to be underlying motor generalization. However, the direct relationship between transfer and generalization has not yet been shown, thereby limiting the assumption that transfer and generalization rely on the same mechanisms. The purpose of this study was to test whether there is a relationship between motor generalization and motor transfer. To date, ten healthy young adult subjects were scored on their motor generalization ability and motor transfer ability on various upper extremity tasks. Although our current sample size is too small to clearly identify whether there is a relationship between generalization and transfer, Pearson product-moment correlation results and a priori power analysis suggest that a significant relationship will be observed with an increased sample size by 30%. If so, this would suggest that the mechanisms of transfer may be similar to those of motor generalization.
ContributorsSohani, Priyanka (Author) / Schaefer, Sydney (Thesis advisor) / Daliri, Ayoub (Committee member) / Honeycutt, Claire (Committee member) / Arizona State University (Publisher)
Created2018
Description
Brain-machine interfaces (BMIs) were first imagined as a technology that would allow subjects to have direct communication with prosthetics and external devices (e.g. control over a computer cursor or robotic arm movement). Operation of these devices was not automatic, and subjects needed calibration and training in order to master this control. In short, learning became a key component in controlling these systems. As a result, BMIs have become ideal tools to probe and explore brain activity, since they allow the isolation of neural inputs and systematic altering of the relationships between the neural signals and output. I have used BMIs to explore the process of brain adaptability in a motor-like task. To this end, I trained non-human primates to control a 3D cursor and adapt to two different perturbations: a visuomotor rotation, uniform across the neural ensemble, and a decorrelation task, which non-uniformly altered the relationship between the activity of particular neurons in an ensemble and movement output. I measured individual and population level changes in the neural ensemble as subjects honed their skills over the span of several days. I found some similarities in the adaptation process elicited by these two tasks. On one hand, individual neurons displayed tuning changes across the entire ensemble after task adaptation: most neurons displayed transient changes in their preferred directions, and most neuron pairs showed changes in their cross-correlations during the learning process. On the other hand, I also measured population level adaptation in the neural ensemble: the underlying neural manifolds that control these neural signals also had dynamic changes during adaptation. I have found that the neural circuits seem to apply an exploratory strategy when adapting to new tasks. Our results suggest that information and trajectories in the neural space increase after initially introducing the perturbations, and before the subject settles into workable solutions. These results provide new insights into both the underlying population level processes in motor learning, and the changes in neural coding which are necessary for subjects to learn to control neuroprosthetics. Understanding of these mechanisms can help us create better control algorithms, and design training paradigms that will take advantage of these processes.
ContributorsArmenta Salas, Michelle (Author) / Helms Tillery, Stephen I (Thesis advisor) / Si, Jennie (Committee member) / Buneo, Christopher (Committee member) / Santello, Marco (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2015
Description
Virtual environments are used for many physical rehabilitation and therapy purposes with varying degrees of success. An important feature for a therapy environment is the real-time monitoring of a participants' movement performance. Such monitoring can be used to evaluate the environment in addition to the participant's learning. Methods for monitoring and evaluation include tracking kinematic performance as well as monitoring muscle and brain activities through EMG and EEG technology. This study aims to observe trends in individual participants' motor learning based on changes in kinematic parameters and use those parameters to characterize different types of learners. This information can then guide EEG/EMG data analysis in the future. The evaluation of motor learning using kinematic parameters of performance typically compares averages of pre- and post-data to identify patterns of changes of various parameters. A key issue with using pre- and post-data is that individual participants perform differently and have different time-courses of learning. Furthermore, different parameters can evolve at independent rates. Finally, there is great variability in the movements at early stages of learning a task. To address these issues, a combined approach is proposed using robust regression, piece-wise regression and correlation to categorize different participant's motor learning. Using the mixed reality rehabilitation system developed at Arizona State University, it was possible to engage participants in motor learning, as revealed by improvements in kinematic parameters. A combination of robust regression, piecewise regression and correlation were used to reveal trends and characterize participants based on motor learning of three kinematic parameters: trajectory error, supination error and the number of phases in the velocity profile.
ContributorsAttygalle, Suneth Satoshi (Author) / He, Jiping (Thesis advisor) / Rikakais, Thanassis (Committee member) / Iasemidis, Leonidas (Committee member) / Arizona State University (Publisher)
Created2010
Description
Repetitive practice of functional movement patterns during motor rehabilitation are known to drive learning (or relearning) of novel motor skills, but the learning process is highly variable between individuals such that responsiveness to task-specific training is often patient-specific. A number of neuroimaging and neurophysiological methods have been proposed to better predict a patient’s responsiveness to a given type or dose of motor therapy. However, these methods are often time- and resource-intensive, and yield results that are not readily interpretable by clinicians. In contrast, standardized visuospatial tests may offer a more feasible solution. The work presented in this dissertation demonstrate that a clinical paper-and-pencil test of visuospatial function may improve predictive models of motor skill learning in older adults and individuals with stroke pathology. To further our understanding of the neuroanatomical correlates underlying this behavioral relationship, I collected diffusion-weighted magnetic resonance images from 19 nondemented older adults to determine if diffusion characteristics of white matter tracts explain shared variance in delayed visuospatial memory test scores and motor skill learning. Consistent with previous work, results indicated that the structural integrity of regions with the bilateral anterior thalamic radiations, corticospinal tracts, and superior longitudinal fasciculi are related to delayed visuospatial memory performance and one-week skill retention. Overall, results of this dissertation suggest that incorporating a clinical paper-and-pencil test of delayed visuospatial memory may prognose motor rehabilitation outcomes and support that personalized variables should be considered in standards of care. Moreover, regions within specific white matter tracts may underlie this behavioral relationship and future work should investigate these regions as potential targets for therapeutic intervention.
ContributorsLingo VanGilder, Jennapher (Author) / Schaefer, Sydney Y (Thesis advisor) / Santello, Marco (Committee member) / Buneo, Christopher (Committee member) / Rogalsky, Corianne (Committee member) / Duff, Kevin (Committee member) / Arizona State University (Publisher)
Created2021
Description
A current thrust in neurorehabilitation research involves exogenous neuromodulation of peripheral nerves to enhance neuroplasticity and maximize recovery of function. This dissertation presents the results of four experiments aimed at assessing the effects of trigeminal nerve stimulation (TNS) and occipital nerve stimulation (ONS) on motor learning, which was behaviorally characterized using an upper extremity visuomotor adaptation paradigm. In Aim 1a, the effects of offline TNS using clinically tested frequencies (120 and 60 Hz) were characterized. Sixty-three participants (22.75±4.6 y/o), performed a visuomotor rotation task and received TNS before encountering rotation of hand visual feedback. In Aim 1b, TNS at 3 kHz, which has been shown to be more tolerable at higher current intensities, was evaluated in 42 additional subjects (23.4±4.6 y/o). Results indicated that 3 kHz stimulation accelerated learning while 60 Hz stimulation slowed learning, suggesting a frequency-dependent effect on learning. In Aim 2, the effect of online TNS using 120 and 60 Hz were characterized to determine if this protocol would deliver better outcomes. Sixty-three participants (23.2±3.9 y/o) received either TNS or sham concurrently with perturbed visual feedback. Results showed no significant differences among groups. However, a cross-study comparison of results obtained with 60 Hz offline TNS showed a statistically significant improvement in learning rates with online stimulation relative to offline, suggesting a timing-dependent effect on learning. In Aim 3, TNS and ONS were compared using the best protocol from previous aims (offline 3 kHz). Additionally, concurrent stimulation of both nerves was explored to look for potential synergistic effects. Eighty-four participants (22.9±3.2 y/o) were assigned to one of four groups: TNS, ONS, TNS+ONS, and sham. Visual inspection of learning curves revealed that the ONS group demonstrated the fastest learning among groups. However, statistical analyses did not confirm this observation. In addition, the TNS+ONS group appeared to learn faster than the sham and TNS groups but slower than the ONS only group, suggesting no synergistic effects using this protocol, as initially hypothesized.
The results provide new information on the potential use of TNS and ONS in neurorehabilitation and performance enhancement in the motor domain.
ContributorsArias, Diego (Author) / Buneo, Christopher (Thesis advisor) / Schaefer, Sydney (Committee member) / Helms-Tillery, Stephen (Committee member) / Santello, Marco (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2023
Description
Research in learning has been conducted for decades, and an area that has received increasing attention since the mid-20th century is motor learning. Since then, new theories and experiments have been developed describing principles of motor learning with parameters that can improve or degrade the learning process. These principles have been applied to many different areas such as psychology, language, and especially sports. Although music involves motor skills, only relatively recently have there been attempts to link these scientific findings with music performance. Given the importance of this area, this document seeks to explore ways in which one may apply principles from motor learning theory to music and more specifically to violin pedagogy. The motor learning principles discussed are based mainly on the studies and theories of Robert Bjork, Cheryl A. Coker, Timothy Lee, Richard Magill, Richard A. Schmidt, and Gabrielle Wulf. The selected topics are focus of attention, practice schedules (discussing blocked and random practice schedules), and variable practice. There are two chapters dedicated to each area. The initial chapter of each topic (two, four, and six) contains a brief literature review that provide a base for application to violin pedagogy. The second chapter of each topic (three, five, and seven) explores those principles along with practical guidelines on how to apply them to violin pedagogy. While some research and experiments in motor learning support pedagogical approaches already used in music (based on the teacher’s intuition and common sense) other studies suggest approaches that are quite counterintuitive. Reviewing a wide variety of practice techniques through a scientific lens provides valuable insights to the field of violin pedagogy and musical performance in general.
ContributorsDa Rocha Unglaub, Alisson (Author) / McLin, Katherine (Thesis advisor) / Jiang, Danwen (Committee member) / Rogers, Rodney (Committee member) / Arizona State University (Publisher)
Created2022
Description
Motor skill learning is important to rehabilitation, sports, and many occupations. When attempting to learn or adapt a motor skill, some individuals learn slower or less compared to others despite the same amount of motor practice. This dissertation aims to understand the factors that contributed to such variability in motor learning, and thereby identify viable methods to enhance motor learning. Behavioral evidence from our lab showed that visuospatial ability is positively related to the extent of motor learning. Neuroimaging studies suggest that motor learning and visuospatial processes share common frontoparietal neural structures, and that this visuospatial-motor relationship may be more pronounced in the right hemisphere compared to the left. Thus, the overall objective of this dissertation is to determine if aspects of motor learning (such as the rate and extent of skill acquisition) may be modifiable through neuromodulation of the right frontoparietal network. In Aim 1, anodal transcranial direct current stimulation (tDCS) was used to test whether modulating the right parietal area affects visuospatial ability and motor skill acquisition. A randomized, three-arm design was used, which added a no-tDCS control group to the double-blinded sham-control protocol to address placebo effects. No tDCS treatment effect was observed, likely due to low statistical power to detect any treatment effects as the study is still ongoing. However, the current results revealed a unique finding that the placebo effect of tDCS was stronger than its treatment effect on motor learning, with implications that tDCS and motor studies should measure and control for placebo effects.
In Aim 2, right frontoparietal connectivity during resting-state EEG was estimated via alpha band imaginary coherence to test whether it correlated with visuospatial performance and motor skill acquisition. As a preliminary step towards leveraging the frontoparietal network for EEG-neurofeedback applications, this work found that alpha imaginary coherence was positively correlated with visuospatial function, but not with motor skill acquisition during a limited dose of motor practice (only 5 trials). This work establishes a premise for developing frontoparietal alpha IC-based neurofeedback for cognitive training in rehabilitation, while warranting future studies to test the relationship between alpha IC and motor learning with a more extensive motor training regimen.
ContributorsWang, Peiyuan (Author) / Schaefer, Sydney Y (Thesis advisor) / Buneo, Christopher A (Committee member) / Abbas, James (Committee member) / Lohse, Keith R (Committee member) / Wyckoff, Sarah N (Committee member) / Arizona State University (Publisher)
Created2021
Description
ABSTRACT
Learning a novel motor pattern through imitation of the skilled performance of an expert has been shown to result in better learning outcomes relative to observational or physical practice. The aim of the present project was to examine if the advantages of imitational practice could be further augmented through a supplementary technique derived from my previous research. This research has provided converging behavioral evidence that dyads engaged in joint action in a familiar task requiring spatial and temporal synchrony end up developing an extended overlap in their body representations, termed a joint body schema (JBS). The present research examined if inducing a JBS between a trainer and a novice trainee, prior to having the dyad engage in imitation practice on a novel motor pattern would enhance both of the training process and its outcomes.
Participants either worked with their trainer on a familiar joint task to develop the JBS (Joint condition) or performed a solo equivalent of the task while being watched by their trainer (Solo condition). Participants In both groups then engaged in blocks of alternating imitation practice and free production of a novel manual motor pattern, while their motor output was recorded. Analyses indicated that the Joint participants outperformed the Solo participants in the ability to synchronize the spatial and temporal components of their imitation movements with the trainer’s pattern-modeling movements. The same group showed superior performance when attempting to freely produce the pattern. These results carry significant theoretical and translational potentials for the fields of motor learning and rehabilitation.
Learning a novel motor pattern through imitation of the skilled performance of an expert has been shown to result in better learning outcomes relative to observational or physical practice. The aim of the present project was to examine if the advantages of imitational practice could be further augmented through a supplementary technique derived from my previous research. This research has provided converging behavioral evidence that dyads engaged in joint action in a familiar task requiring spatial and temporal synchrony end up developing an extended overlap in their body representations, termed a joint body schema (JBS). The present research examined if inducing a JBS between a trainer and a novice trainee, prior to having the dyad engage in imitation practice on a novel motor pattern would enhance both of the training process and its outcomes.
Participants either worked with their trainer on a familiar joint task to develop the JBS (Joint condition) or performed a solo equivalent of the task while being watched by their trainer (Solo condition). Participants In both groups then engaged in blocks of alternating imitation practice and free production of a novel manual motor pattern, while their motor output was recorded. Analyses indicated that the Joint participants outperformed the Solo participants in the ability to synchronize the spatial and temporal components of their imitation movements with the trainer’s pattern-modeling movements. The same group showed superior performance when attempting to freely produce the pattern. These results carry significant theoretical and translational potentials for the fields of motor learning and rehabilitation.
ContributorsSoliman, Tamer (Author) / Glenberg, Arthur (Thesis advisor) / Helms Tillery, Stephen (Committee member) / McBeath, Michael (Committee member) / Amazeen, Eric (Committee member) / Arizona State University (Publisher)
Created2015