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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
Random peptide microarrays are a powerful tool for both the treatment and diagnostics of infectious diseases. On the treatment side, selected random peptides on the microarray have either binding or lytic potency against certain pathogens cells, thus they can be synthesized into new antimicrobial agents, denoted as synbodies (synthetic antibodies). On the diagnostic side, serum containing specific infection-related antibodies create unique and distinct "pathogen-immunosignatures" on the random peptide microarray distinct from the healthy control serum, and this different mode of binding can be used as a more precise measurement than traditional ELISA tests. My thesis project is separated into these two parts: the first part falls into the treatment side and the second one focuses on the diagnostic side. My first chapter shows that a substitution amino acid peptide library helps to improve the activity of a recently reported synthetic antimicrobial peptide selected by the random peptide microarray. By substituting one or two amino acids of the original lead peptide, the new substitutes show changed hemolytic effects against mouse red blood cells and changed potency against two pathogens: Staphylococcus aureus and Pseudomonas aeruginosa. Two new substitutes are then combined together to form the synbody, which shows a significantly antimicrobial potency against Staphylococcus aureus (<0.5uM). In the second chapter, I explore the possibility of using the 10K Ver.2 random peptide microarray to monitor the humoral immune response of dengue. Over 2.5 billion people (40% of the world's population) live in dengue transmitting areas. However, currently there is no efficient dengue treatment or vaccine. Here, with limited dengue patient serum samples, we show that the immunosignature has the potential to not only distinguish the dengue infection from non-infected people, but also the primary dengue infection from the secondary dengue infections, dengue infection from West Nile Virus (WNV) infection, and even between different dengue serotypes. By further bioinformatic analysis, we demonstrate that the significant peptides selected to distinguish dengue infected and normal samples may indicate the epitopes responsible for the immune response.
ContributorsWang, Xiao (Author) / Johnston, Stephen Albert (Thesis advisor) / Blattman, Joseph (Committee member) / Arntzen, Charles (Committee member) / Arizona State University (Publisher)
Created2013
Description
Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems. First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance. We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking. For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate compressive sensing with PF to estimate neural activity. Simulation results show that both sensor scheduling and compressive sensing based methods achieve comparable tracking performance with significantly reduced number of sensors.
ContributorsMiao, Lifeng (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Zhang, Junshan (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Humans' ability to perform fine object and tool manipulation is a defining feature of their sensorimotor repertoire. How the central nervous system builds and maintains internal representations of such skilled hand-object interactions has attracted significant attention over the past three decades. Nevertheless, two major gaps exist: a) how digit positions and forces are coordinated during natural manipulation tasks, and b) what mechanisms underlie the formation and retention of internal representations of dexterous manipulation. This dissertation addresses these two questions through five experiments that are based on novel grip devices and experimental protocols. It was found that high-level representation of manipulation tasks can be learned in an effector-independent fashion. Specifically, when challenged by trial-to-trial variability in finger positions or using digits that were not previously engaged in learning the task, subjects could adjust finger forces to compensate for this variability, thus leading to consistent task performance. The results from a follow-up experiment conducted in a virtual reality environment indicate that haptic feedback is sufficient to implement the above coordination between digit position and forces. However, it was also found that the generalizability of a learned manipulation is limited across tasks. Specifically, when subjects learned to manipulate the same object across different contexts that require different motor output, interference was found at the time of switching contexts. Data from additional studies provide evidence for parallel learning processes, which are characterized by different rates of decay and learning. These experiments have provided important insight into the neural mechanisms underlying learning and control of object manipulation. The present findings have potential biomedical applications including brain-machine interfaces, rehabilitation of hand function, and prosthetics.
ContributorsFu, Qiushi (Author) / Santello, Marco (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Santos, Veronica (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2013
Description
Reaching movements are subject to noise in both the planning and execution phases of movement production. Although the effects of these noise sources in estimating and/or controlling endpoint position have been examined in many studies, the independent effects of limb configuration on endpoint variability have been largely ignored. The present study investigated the effects of arm configuration on the interaction between planning noise and execution noise. Subjects performed reaching movements to three targets located in a frontal plane. At the starting position, subjects matched one of two desired arm configuration 'templates' namely "adducted" and "abducted". These arm configurations were obtained by rotations along the shoulder-hand axis, thereby maintaining endpoint position. Visual feedback of the hand was varied from trial to trial, thereby increasing uncertainty in movement planning and execution. It was hypothesized that 1) pattern of endpoint variability would be dependent on arm configuration and 2) that these differences would be most apparent in conditions without visual feedback. It was found that there were differences in endpoint variability between arm configurations in both visual conditions, but these differences were much larger when visual feedback was withheld. The overall results suggest that patterns of endpoint variability are highly dependent on arm configuration, particularly in the absence of visual feedback. This suggests that in the presence of vision, movement planning in 3D space is performed using coordinates that are largely arm configuration independent (i.e. extrinsic coordinates). In contrast, in the absence of vision, movement planning in 3D space reflects a substantial contribution of intrinsic coordinates.
ContributorsLakshmi Narayanan, Kishor (Author) / Buneo, Christopher (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Community college students are attracted to courses with alternative delivery formats such as hybrid courses because the more flexible delivery associated with such courses provides convenience for busy students. In a hybrid course, face-to-face, structured seat time is exchanged for online components. In such courses, students take more responsibility for their learning because they assume additional responsibility for learning more of the course material on their own. Thus, self-regulated learning (SRL) behaviors have the potential to be useful for students to successfully navigate hybrid courses because the online components require exercise of more personal control over the autonomous learning situations inherent in hybrid courses. Self-regulated learning theory includes three components: metacognition, motivation, and behavioral actions. In the current study, this theoretical framework is used to examine how inducing self-regulated learning activities among students taking a hybrid course influence performance in a community college science course. The intervention for this action research study consisted of a suite of activities that engage students in self-regulated learning behaviors to foster student performance. The specific SRL activities included predicting grades, reflections on coursework and study efforts in course preparation logs, explanation of SRL procedures in response to a vignette, photo ethnography work on their personal use of SRL approaches, and a personalized study plan. A mixed method approach was employed to gather evidence for the study. Results indicate that community college students use a variety of self-regulated learning strategies to support their learning of course material. Further, engaging community college students in learning reflection activities appears to afford some students with opportunities to refine their SRL skills and influence their learning. The discussion focuses on integrating the quantitative and qualitative data and explanation of the findings using the SRL framework. Additionally, lessons learned, limitations, and implications for practice and research are discussed. Specifically, it is suggested that instructors can foster student learning in hybrid courses by teaching students to engage in SRL processes and behaviors rather than merely focusing on delivery of course content. Such SRL behaviors allow students to exercise greater control over the autonomous learning situations inherent in hybrid courses.
ContributorsManuelito, Shannon Joy (Author) / Buss, Ray R. (Thesis advisor) / Smith, Rachel (Committee member) / Barnett, Joshua (Committee member) / Arizona State University (Publisher)
Created2013
Description
Image resolution limits the extent to which zooming enhances clarity, restricts the size digital photographs can be printed at, and, in the context of medical images, can prevent a diagnosis. Interpolation is the supplementing of known data with estimated values based on a function or model involving some or all of the known samples. The selection of the contributing data points and the specifics of how they are used to define the interpolated values influences how effectively the interpolation algorithm is able to estimate the underlying, continuous signal. The main contributions of this dissertation are three fold: 1) Reframing edge-directed interpolation of a single image as an intensity-based registration problem. 2) Providing an analytical framework for intensity-based registration using control grid constraints. 3) Quantitative assessment of the new, single-image enlargement algorithm based on analytical intensity-based registration. In addition to single image resizing, the new methods and analytical approaches were extended to address a wide range of applications including volumetric (multi-slice) image interpolation, video deinterlacing, motion detection, and atmospheric distortion correction. Overall, the new approaches generate results that more accurately reflect the underlying signals than less computationally demanding approaches and with lower processing requirements and fewer restrictions than methods with comparable accuracy.
ContributorsZwart, Christine M. (Author) / Frakes, David H (Thesis advisor) / Karam, Lina (Committee member) / Kodibagkar, Vikram (Committee member) / Spanias, Andreas (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2013
Description
Biological diversity is threatened by increasing anthropogenic modification of natural environments and increasing demands on natural resources. Sonoran desert tortoises (Gopherus morafkai) currently have Candidate status under the Endangered Species Act (ESA) based on health and habitat threats. To ensure this animal persists in the midst of multiple threats requires an understanding of the life history and ecology of each population. I looked at one physiological and one behavioral aspect of a population of tortoises at the Sugarloaf Mountain (SL) study site in central Arizona, USA. I used 21 years of capture-recapture records to estimate growth parameters of the entire population. I investigated habitat selection of juvenile tortoises by selecting 117 locations of 11 tortoises that had been tracked by radio-telemetry one to three times weekly for two years, selecting locations from both summer active season and during winter hibernation. I compared 22 microhabitat variables of tortoise locations to random SL locations to determine habitat use and availability. Male tortoises at SL reach a greater asymptotic length than females, and males and females appear to grow at the same rate. Juvenile tortoises at the SL site use steep rocky hillsides with high proportions of sand and annual vegetation, few succulents, and enclosed shelters in summer. They use enclosed shelters on steep slopes for winter hibernation. An understanding of these features can allow managers to quantify Sonoran desert tortoise habitat needs and life history characteristics and to understand the impact of land use policies.
ContributorsBridges, Andrew (Author) / Bateman, Heather L (Thesis advisor) / Miller, William (Committee member) / Ulrich, Jon (Committee member) / Arizona State University (Publisher)
Created2012
Description
At the heart of every eusocial insect colony is a reproductive division of labor. This division can emerge through dominance interactions at the adult stage or through the production of distinct queen and worker castes at the larval stage. In both cases, this division depends on plasticity within an individual to develop reproductive characteristics or serve as a worker. In order to gain insight into the evolution of reproductive plasticity in the social insects, I investigated caste determination and dominance in the ant Harpegnathos saltator, a species that retains a number of ancestral characteristics. Treatment of worker larvae with a juvenile hormone (JH) analog induced late-instar larvae to develop as queens. At the colony level, workers must have a mechanism to regulate larval development to prevent queens from developing out of season. I identified a new behavior in H. saltator where workers bite larvae to inhibit queen determination. Workers could identify larval caste based on a chemical signal specific to queen-destined larvae, and the production of this signal was directly linked to increased JH levels. This association provides a connection between the physiological factors that induce queen development and the production of a caste-specific larval signal. In addition to caste determination at the larval stage, adult workers of H. saltator compete to establish a reproductive hierarchy. Unlike other social insects, dominance in H. saltator was not related to differences in JH or ecdysteroid levels. Instead, changes in brain levels of biogenic amines, particularly dopamine, were correlated with dominance and reproductive status. Receptor genes for dopamine were expressed in both the brain and ovaries of H. saltator, and this suggests that dopamine may coordinate changes in behavior at the neurological level with ovarian status. Together, these studies build on our understanding of reproductive plasticity in social insects and provide insight into the evolution of a reproductive division of labor.
ContributorsPenick, Clint A (Author) / Liebig, Jürgen (Thesis advisor) / Brent, Colin (Committee member) / Gadau, Jürgen (Committee member) / Hölldobler, Bert (Committee member) / Rutowski, Ron (Committee member) / Arizona State University (Publisher)
Created2012
Description
The object of this study is to charac terize the effect of focused ultrasound stimulation (FUS) on the rat ce rvix which has been observed to speed its ripening during pregnancy. Ce rvical ripening is required for successful fetal delivery. Timed-pregnant Sprague-Dawley rats (n=36) were used. On day 14 of gestation, the FUS system was placed on the body surface of the rat over the cervix and ultrasound energy was applied to cervix for variable times up to 1 hour in the control group, the FUS system was placed on rats but no energy was applied. Daily measurement of cervix light-induced florescence (LIF, photon counts of collagen x-bridge fluorescence) were made on days 16 of gestation and daily until spont-aneous delivery (day22) to estimate changes in cervical ripening. We found that pulses of 680 KHz ultrasound at 25 Hertz, 1 millisecond pulse duration at 1W/cm^2 applied for as little as 30 minutes would immediately afterwards show the cervix to hav e ripened to the degree seen just before delivery on day 22. Delivery times, fetal weights and viability were unaffected in the FUS-treated animals.
ContributorsLuo, Daishen (Author) / Towe, Bruce C (Thesis advisor) / Wang, Xiao (Committee member) / Caplan, Michael (Committee member) / Arizona State University (Publisher)
Created2012