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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

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
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Description
Our ability to estimate the position of our body parts in space, a fundamentally proprioceptive process, is crucial for interacting with the environment and movement control. For proprioception to support these actions, the Central Nervous System has to rely on a stored internal representation of the body parts in space.

Our ability to estimate the position of our body parts in space, a fundamentally proprioceptive process, is crucial for interacting with the environment and movement control. For proprioception to support these actions, the Central Nervous System has to rely on a stored internal representation of the body parts in space. However, relatively little is known about this internal representation of arm position. To this end, I developed a method to map proprioceptive estimates of hand location across a 2-d workspace. In this task, I moved each subject's hand to a target location while the subject's eyes were closed. After returning the hand, subjects opened their eyes to verbally report the location of where their fingertip had been. Then, I reconstructed and analyzed the spatial structure of the pattern of estimation errors. In the first couple of experiments I probed the structure and stability of the pattern of errors by manipulating the hand used and tactile feedback provided when the hand was at each target location. I found that the resulting pattern of errors was systematically stable across conditions for each subject, subject-specific, and not uniform across the workspace. These findings suggest that the observed structure of pattern of errors has been constructed through experience, which has resulted in a systematically stable internal representation of arm location. Moreover, this representation is continuously being calibrated across the workspace. In the next two experiments, I aimed to probe the calibration of this structure. To this end, I used two different perturbation paradigms: 1) a virtual reality visuomotor adaptation to induce a local perturbation, 2) and a standard prism adaptation paradigm to induce a global perturbation. I found that the magnitude of the errors significantly increased to a similar extent after each perturbation. This small effect indicates that proprioception is recalibrated to a similar extent regardless of how the perturbation is introduced, suggesting that sensory and motor changes may be two independent processes arising from the perturbation. Moreover, I propose that the internal representation of arm location might be constructed with a global solution and not capable of local changes.
ContributorsRincon Gonzalez, Liliana (Author) / Helms Tillery, Stephen I (Thesis advisor) / Buneo, Christopher A (Thesis advisor) / Santello, Marco (Committee member) / Santos, Veronica (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2012
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Description
The ability to plan, execute, and control goal oriented reaching and grasping movements is among the most essential functions of the brain. Yet, these movements are inherently variable; a result of the noise pervading the neural signals underlying sensorimotor processing. The specific influences and interactions of these noise processes remain

The ability to plan, execute, and control goal oriented reaching and grasping movements is among the most essential functions of the brain. Yet, these movements are inherently variable; a result of the noise pervading the neural signals underlying sensorimotor processing. The specific influences and interactions of these noise processes remain unclear. Thus several studies have been performed to elucidate the role and influence of sensorimotor noise on movement variability. The first study focuses on sensory integration and movement planning across the reaching workspace. An experiment was designed to examine the relative contributions of vision and proprioception to movement planning by measuring the rotation of the initial movement direction induced by a perturbation of the visual feedback prior to movement onset. The results suggest that contribution of vision was relatively consistent across the evaluated workspace depths; however, the influence of vision differed between the vertical and later axes indicate that additional factors beyond vision and proprioception influence movement planning of 3-dimensional movements. If the first study investigated the role of noise in sensorimotor integration, the second and third studies investigate relative influence of sensorimotor noise on reaching performance. Specifically, they evaluate how the characteristics of neural processing that underlie movement planning and execution manifest in movement variability during natural reaching. Subjects performed reaching movements with and without visual feedback throughout the movement and the patterns of endpoint variability were compared across movement directions. The results of these studies suggest a primary role of visual feedback noise in shaping patterns of variability and in determining the relative influence of planning and execution related noise sources. The final work considers a computational approach to characterizing how sensorimotor processes interact to shape movement variability. A model of multi-modal feedback control was developed to simulate the interaction of planning and execution noise on reaching variability. The model predictions suggest that anisotropic properties of feedback noise significantly affect the relative influence of planning and execution noise on patterns of reaching variability.
ContributorsApker, Gregory Allen (Author) / Buneo, Christopher A (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Santello, Marco (Committee member) / Santos, Veronica (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Electrical nerve stimulation is a promising drug-free technology that could treat a variety of ailments and disorders. Methods like Vagus Nerve Stimulation have been used for decades to treat disorders like epilepsy, and research with non-invasive vagus nerve stimulation has shown similar effects as its invasive counterpart. Non-invasive nerve stimulation

Electrical nerve stimulation is a promising drug-free technology that could treat a variety of ailments and disorders. Methods like Vagus Nerve Stimulation have been used for decades to treat disorders like epilepsy, and research with non-invasive vagus nerve stimulation has shown similar effects as its invasive counterpart. Non-invasive nerve stimulation methods like vagus nerve stimulation could help millions of people treat and manage various disorders.

This study observed the effects of three different non-invasive nerve stimulation paradigms in human participants. The first study analyzed the safety and efficacy of transcutaneous auricular vagal nerve stimulation in healthy humans using a bilateral stimulation protocol with uniquely designed dry-hydrogel electrodes. Results demonstrate bilateral auricular vagal nerve stimulation has significant effects on specific parameters of autonomic activity and is safe and well tolerated. The second study analyzed the effects of non-invasive electrical stimulation of a region on the side of the neck that contains the Great Auricular Nerve and the Auricular Branch of the Vagus Nerve called the tympanomastoid fissure on golf hitting performance in healthy golfers. Results did not show significant effects on hitting performance or physiological activity, but the nerve stimulation had significant effects on reducing state-anxiety and improving the quality of feel of each shot. The third study analyzed the effects of non-invasive nerve stimulation of cervical nerves on the back of the neck on putting performance of yips-affected golfers. Results demonstrated that cervical nerve stimulation had significant effects on improving putting performance but did not have significant effects on physiological activity. Data from these studies show there are potential applications for non-invasive electrical nerve stimulation for healthy and athletic populations. Future research should also examine the effects of these stimulation methods in clinical populations.
ContributorsHool, Nicholas (Author) / Tyler, William J (Thesis advisor) / Crews, Debbie (Committee member) / Muthuswamy, Jitendran (Committee member) / Helms Tillery, Stephen (Committee member) / Sebold, Brent (Committee member) / Arizona State University (Publisher)
Created2020
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Description
Transcranial focused ultrasound (tFUS) is a unique neurostimulation modality with potential to develop into a highly sophisticated and effective tool. Unlike any other noninvasive neurostimulation technique, tFUS has a high spatial resolution (on the order of millimeters) and can penetrate across the skull, deep into the brain. Sub-thermal tFUS has

Transcranial focused ultrasound (tFUS) is a unique neurostimulation modality with potential to develop into a highly sophisticated and effective tool. Unlike any other noninvasive neurostimulation technique, tFUS has a high spatial resolution (on the order of millimeters) and can penetrate across the skull, deep into the brain. Sub-thermal tFUS has been shown to induce changes in EEG and fMRI, as well as perception and mood. This study investigates the possibility of using tFUS to modulate brain networks involved in attention and cognitive control.Three different brain areas linked to saliency, cognitive control, and emotion within the cingulo-opercular network were stimulated with tFUS while subjects performed behavioral paradigms. The first study targeted the dorsal anterior cingulate cortex (dACC), which is associated with performance on cognitive attention tasks, conflict, error, and, emotion. Subjects performed a variant of the Erikson Flanker task in which emotional faces (fear, neutral or scrambled) were displayed in the background as distractors. tFUS significantly reduced the reaction time (RT) delay induced by faces; there were significant differences between tFUS and Sham groups in event related potentials (ERP), event related spectral perturbation (ERSP), conflict and error processing, and heart rate variability (HRV).
The second study used the same behavioral paradigm, but targeted tFUS to the right anterior insula/frontal operculum (aIns/fO). The aIns/fO is implicated in saliency, cognitive control, interoceptive awareness, autonomic function, and emotion. tFUS was found to significantly alter ERP, ERSP, conflict and error processing, and HRV responses.
The third study targeted tFUS to the right inferior frontal gyrus (rIFG), employing the Stop Signal task to study inhibition. tFUS affected ERPs and improved stopping speed. Using network modeling, causal evidence is presented for rIFG influence on subcortical nodes in stopping.
This work provides preliminarily evidence that tFUS can be used to modulate broader network function through a single node, affecting neurophysiological processing, physiologic responses, and behavioral performance. Additionally it can be used as a tool to elucidate network function. These studies suggest tFUS has the potential to affect cognitive function as a clinical tool, and perhaps even enhance wellbeing and expand conscious awareness.
ContributorsFini, Maria Elizabeth (Author) / Tyler, William J (Thesis advisor) / Greger, Bradley (Committee member) / Santello, Marco (Committee member) / Kleim, Jeffrey (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2020