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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
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
Often learning new skills, such as how to throw a basketball or how to play the piano, are better accomplished practicing with another than from self-practice. Why? We propose that during joint action, partners learn to adjust their behavior to each other. For example, when dancing with a partner, we must adjust the timing, the force, and the spatial locations of movements to those of the partner. We call these adjustments a joint body schema (JBS). That is, the locations of our own effectors and our own movements are adapted by interaction with the partner. Furthermore, we propose that after a JBS is established, learning new motor skills can be enhanced by the learner's attunement to the specifics of the partner's actions. We test this proposal by having partners engage in a motor task requiring cooperation (to develop the JBS). Then we determined whether a) the JBS enhances the coordination on an unrelated task, and b) whether the JBS enhances the learning of a new motor skill. In fact, participants who established a JBS showed stronger coordination with a partner and better motor learning from the partner than did control participants. Several applications of this finding are discussed.
ContributorsMunion, Amanda Kathleen (Author) / Glenberg, Arthur (Thesis director) / Knight, George (Committee member) / McBeath, Michael (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Psychology (Contributor)
Created2015-05
Description
The primary motor cortex (M1) plays a vital role in motor planning and execution, as well as in motor learning. Baseline corticospinal excitability (CSE) in M1 is known to increase as a result of motor learning, but less is understand about the modulation of CSE at the pre-execution planning stage due to learning. This question was addressed using single pulse transcranial magnetic stimulation (TMS) to measure the modulation of both baseline and planning CSE due to learning a reach to grasp task. It was hypothesized that baseline CSE would increase and planning CSE decrease as a function of trial; an increase in baseline CSE would replicate established findings in the literature, while a decrease in planning would be a novel finding. Eight right-handed subjects were visually cued to exert a precise grip force, with the goal of producing that force accurately and consistently. Subjects effectively learned the task in the first 10 trials, but no significant trends were found in the modulation of baseline or planning CSE. The lack of significant results may be due to the very quick learning phase or the lower intensity of training as compared to past studies. The findings presented here suggest that planning and baseline CSE may be modulated along different time courses as learning occurs and point to some important considerations for future studies addressing this question.
ContributorsMoore, Dalton Dale (Author) / Santello, Marco (Thesis director) / Kleim, Jeff (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2015-05
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
ContributorsEvans, Bartlett R. (Conductor) / Schildkret, David (Conductor) / Glenn, Erica (Conductor) / Concert Choir (Performer) / Chamber Singers (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-16
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
ContributorsOwen, Ken (Conductor) / McDevitt, Mandy L. M. (Performer) / Larson, Brook (Conductor) / Wang, Lin-Yu (Performer) / Jacobs, Todd (Performer) / Morehouse, Daniel (Performer) / Magers, Kristen (Performer) / DeGrow, Gary (Performer) / DeGrow, Richard (Performer) / Women's Chorus (Performer) / Sun Devil Singers (Performer) / ASU Library. Music Library (Publisher)
Created2004-03-24
ContributorsMetz, John (Performer) / Sowers, Richard (Performer) / Collegium Musicum (Performer) / ASU Library. Music Library (Publisher)
Created1983-01-29