Matching Items (9)
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- All Subjects: Biomedical Engineering
- Genre: Doctoral Dissertation
- Creators: Greger, Bradley
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
Epilepsy is a group of disorders that cause seizures in approximately 2.2 million people in the United States. Over 30% of these patients have epilepsies that do not respond to treatment with anti-epileptic drugs. For this population, focal resection surgery could offer long-term seizure freedom. Surgery candidates undergo a myriad of tests and monitoring to determine where and when seizures occur. The “gold standard” method for focus identification involves the placement of electrocorticography (ECoG) grids in the sub-dural space, followed by continual monitoring and visual inspection of the patient’s cortical activity. This process, however, is highly subjective and uses dated technology. Multiple studies were performed to investigate how the evaluation process could benefit from an algorithmic adjust using current ECoG technology, and how the use of new microECoG technology could further improve the process.
Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures.
Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids.
Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes.
Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures.
Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids.
Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes.
ContributorsAshmont, Kari Rich (Author) / Greger, Bradley (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Adelson, P David (Committee member) / Dudek, F Edward (Committee member) / Arizona State University (Publisher)
Created2015
Description
Object manipulation is a common sensorimotor task that humans perform to interact with the physical world. The first aim of this dissertation was to characterize and identify the role of feedback and feedforward mechanisms for force control in object manipulation by introducing a new feature based on force trajectories to quantify the interaction between feedback- and feedforward control. This feature was applied on two grasp contexts: grasping the object at either (1) predetermined or (2) self-selected grasp locations (“constrained” and “unconstrained”, respectively), where unconstrained grasping is thought to involve feedback-driven force corrections to a greater extent than constrained grasping. This proposition was confirmed by force feature analysis. The second aim of this dissertation was to quantify whether force control mechanisms differ between dominant and non-dominant hands. The force feature analysis demonstrated that manipulation by the dominant hand relies on feedforward control more than the non-dominant hand. The third aim was to quantify coordination mechanisms underlying physical interaction by dyads in object manipulation. The results revealed that only individuals with worse solo performance benefit from interpersonal coordination through physical couplings, whereas the better individuals do not. This work showed that naturally emerging leader-follower roles, whereby the leader in dyadic manipulation exhibits significant greater force changes than the follower. Furthermore, brain activity measured through electroencephalography (EEG) could discriminate leader and follower roles as indicated power modulation in the alpha frequency band over centro-parietal areas. Lastly, this dissertation suggested that the relation between force and motion (arm impedance) could be an important means for communicating intended movement direction between biological agents.
ContributorsMojtahedi, Keivan (Author) / Santello, Marco (Thesis advisor) / Greger, Bradley (Committee member) / Artemiadis, Panagiotis (Committee member) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Arizona State University (Publisher)
Created2017
Description
In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.
ContributorsPadmanaban, Subash (Author) / Greger, Bradley (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Crook, Sharon (Committee member) / Arizona State University (Publisher)
Created2017
Description
Prosthetic users abandon devices due to difficulties performing tasks without proper graded or interpretable feedback. The inability to adequately detect and correct error of the device leads to failure and frustration. In advanced prostheses, peripheral nerve stimulation can be used to deliver sensations, but standard schemes used in sensorized prosthetic systems induce percepts inconsistent with natural sensations, providing limited benefit. Recent uses of time varying stimulation strategies appear to produce more practical sensations, but without a clear path to pursue improvements. This dissertation examines the use of physiologically based stimulation strategies to elicit sensations that are more readily interpretable. A psychophysical experiment designed to investigate sensitivities to the discrimination of perturbation direction within precision grip suggests that perception is biomechanically referenced: increased sensitivities along the ulnar-radial axis align with potential anisotropic deformation of the finger pad, indicating somatosensation uses internal information rather than environmental. Contact-site and direction dependent deformation of the finger pad activates complimentary fast adapting and slow adapting mechanoreceptors, exhibiting parallel activity of the two associate temporal patterns: static and dynamic. The spectrum of temporal activity seen in somatosensory cortex can be explained by a combined representation of these distinct response dynamics, a phenomenon referred in this dissertation to “biphasic representation.” In a reach-to-precision-grasp task, neurons in somatosensory cortex were found to possess biphasic firing patterns in their responses to texture, orientation, and movement. Sensitivities seem to align with variable deformation and mechanoreceptor activity: movement and smooth texture responses align with potential fast adapting activation, non-movement and coarse texture responses align with potential increased slow adapting activation, and responses to orientation are conceptually consistent with coding of tangential load. Using evidence of biphasic representations’ association with perceptual priorities, gamma band phase locking is used to compare responses to peripheral nerve stimulation patterns and mechanical stimulation. Vibrotactile and punctate mechanical stimuli are used to represent the practical and impractical percepts commonly observed in peripheral nerve stimulation feedback. Standard patterns of constant parameters closely mimic impractical vibrotactile stimulation while biphasic patterns better mimic punctate stimulation and provide a platform to investigate intragrip dynamics representing contextual activation.
ContributorsTanner, Justin Cody (Author) / Helms Tillery, Stephen I (Thesis advisor) / Santos, Veronica J (Committee member) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Buneo, Christopher A (Committee member) / Arizona State University (Publisher)
Created2017
Description
Information processing in the brain is mediated by network interactions between anatomically distant (centimeters apart) regions of cortex and network action is fundamental to human behavior. Disruptive activity of these networks may allow a variety of diseases to develop. Degradation or loss of network function in the brain can affect many aspects of the human experience; motor disorder, language difficulties, memory loss, mood swings, and more. The cortico-basal ganglia loop is a system of networks in the brain between the cortex, basal ganglia, the thalamus, and back to the cortex. It is not one singular circuit, but rather a series of parallel circuits that are relevant towards motor output, motor planning, and motivation and reward. Studying the relationship between basal ganglia neurons and cortical local field potentials may lead to insights about neurodegenerative diseases and how these diseases change the cortico-basal ganglia circuit. Speech and language are uniquely human and require the coactivation of several brain regions. The various aspects of language are spread over the temporal lobe and parts of the occipital, parietal, and frontal lobe. However, the core network for speech production involves collaboration between phonologic retrieval (encoding ideas into syllabic representations) from Wernicke’s area, and phonemic encoding (translating syllables into motor articulations) from Broca’s area. Studying the coactivation of these brain regions during a repetitive speech production task may lead to a greater understanding of their electrophysiological functional connectivity. The primary purpose of the work presented in this document is to validate the use of subdural microelectrodes in electrophysiological functional connectivity research as these devices best match the spatial and temporal scales of brain activity. Neuron populations in the cortex are organized into functional units called cortical columns. These cortical columns operate on the sub-millisecond temporal and millimeter spatial scale. The study of brain networks, both in healthy and unwell individuals, may reveal new methodologies of treatment or management for disease and injury, as well as contribute to our scientific understanding of how the brain works.
ContributorsO'Neill, Kevin John (Author) / Greger, Bradley (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Papandreou-Suppapola, Antonia (Committee member) / Kleim, Jeffery (Committee member) / Arizona State University (Publisher)
Created2021
Description
Multisensory integration is the process by which information from different sensory modalities is integrated by the nervous system. This process is important not only from a basic science perspective but also for translational reasons, e.g., for the development of closed-loop neural prosthetic systems. A mixed virtual reality platform was developed to study the neural mechanisms of multisensory integration for the upper limb during motor planning. The platform allows for selection of different arms and manipulation of the locations of physical and virtual target cues in the environment. The system was tested with two non-human primates (NHP) trained to reach to multiple virtual targets. Arm kinematic data as well as neural spiking data from primary motor (M1) and dorsal premotor cortex (PMd) were collected. The task involved manipulating visual information about initial arm position by rendering the virtual avatar arm in either its actual position (veridical (V) condition) or in a different shifted (e.g., small vs large shifts) position (perturbed (P) condition) prior to movement. Tactile feedback was modulated in blocks by placing or removing the physical start cue on the table (tactile (T), and no-tactile (NT) conditions, respectively). Behaviorally, errors in initial movement direction were larger when the physical start cue was absent. Slightly larger directional errors were found in the P condition compared to the V condition for some movement directions. Both effects were consistent with the idea that erroneous or reduced information about initial hand location led to movement direction-dependent reach planning errors. Neural correlates of these behavioral effects were probed using population decoding techniques. For small shifts in the visual position of the arm, no differences in decoding accuracy between the T and NT conditions were observed in either M1 or PMd. However, for larger visual shifts, decoding accuracy decreased in the NT condition, but only in PMd. Thus, activity in PMd, but not M1, may reflect the uncertainty in reach planning that results when sensory cues regarding initial hand position are erroneous or absent.
ContributorsPhataraphruk, Preyaporn Kris (Author) / Buneo, Christopher A (Thesis advisor) / Zhou, Yi (Committee member) / Helms Tillery, Steve (Committee member) / Greger, Bradley (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2023
Description
Safety and efficacy of neuromodulation are influenced by abiotic factors like failure of implants, biotic factors like tissue damage, and molecular and cellular mechanisms of neuromodulation. Accelerated lifetime test (ALT) predict lifetime of implants by accelerating failure modes in controlled bench-top conditions. Current ALT models do not capture failure modes involving biological mechanisms. First part of this dissertation is focused on developing ALTs for predicting failure of chronically implanted tungsten stimulation electrodes. Three factors used in ALT are temperature, H2O2 concentration, and amount of charge delivered through electrode to develop a predictive model of lifetime for stimulation electrodes. Second part of this dissertation is focused on developing a novel method for evaluating tissue response to implants and electrical stimulation. Current methods to evaluate tissue damage in the brain require invasive and terminal procedures that have poor clinical translation. I report a novel non-invasive method that sampled peripheral blood monocytes (PBMCs) and used enzyme-linked immunoassay (ELISA) to assess level of glial fibrillary acidic protein (GFAP) expression and fluorescence-activated cell sorting (FACS) to quantify number of GFAP expressing PBMCs. Using this method, I was able to detect and quantify GFAP expression in PBMCs. However, there was no statistically significant difference in GFAP expression between stimulatory and non-stimulatory implants. Final part of this dissertation assessed molecular and cellular mechanisms of non-invasive ultrasound neuromodulation approach. Unlike electrical stimulation, cellular mechanisms of ultrasound-based neuromodulation are not fully known. Final part of this dissertation assessed role of mechanosensitive ion channels and neuronal nitric oxide production in cell cultures under ultrasound excitation. I used fluorescent imaging to quantify expression of nitric oxide in neuronal cell cultures in response to ultrasound stimulation. Results from these experiments indicate that neuronal nitric oxide production increased in response to ultrasound stimulation compared to control and decreased when mechanosensitive ion channels were suppressed. Two novel methods developed in this dissertation enable assessment of lifetime and safety of neuromodulation techniques that use electrical stimulation through implants. The final part of this dissertation concludes that non-invasive ultrasound neuromodulation may be mediated through neuronal nitric oxide even in absence of activation of mechanosensitive ion channels.
ContributorsVoziyanov, Vladislav (Author) / Muthuswamy, Jitendran (Thesis advisor) / Smith, Barbara (Committee member) / Greger, Bradley (Committee member) / Abbas, James (Committee member) / Okandan, Murat (Committee member) / Arizona State University (Publisher)
Created2022
Description
Electromagnetic fields (EMFs) generated by biologically active neural tissue are critical in the diagnosis and treatment of neurological diseases. Biological EMFs are characterized by electromagnetic properties such as electrical conductivity, permittivity and magnetic susceptibility. The electrical conductivity of active tissue has been shown to serve as a biomarker for the direct detection of neural activity, and the diagnosis, staging and prognosis of disease states such as cancer. Magnetic resonance electrical impedance tomography (MREIT) was developed to map the cross-sectional conductivity distribution of electrically conductive objects using externally applied electrical currents. Simulation and in vitro studies of invertebrate neural tissue complexes demonstrated the correlation of membrane conductivity variations with neural activation levels using the MREIT technique, therefore laying the foundation for functional MREIT (fMREIT) to detect neural activity, and future in vivo fMREIT studies.
The development of fMREIT for the direct detection of neural activity using conductivity contrast in in vivo settings has been the focus of the research work presented here. An in vivo animal model was developed to detect neural activity initiated changes in neuronal membrane conductivities under external electrical current stimulation. Neural activity was induced in somatosensory areas I (SAI) and II (SAII) by applying electrical currents between the second and fourth digits of the rodent forepaw. The in vivo animal model involved the use of forepaw stimulation to evoke somatosensory neural activations along with hippocampal fMREIT imaging currents contemporaneously applied under magnetic field strengths of 7 Tesla. Three distinct types of fMREIT current waveforms were applied as imaging currents under two inhalants – air and carbogen. Active regions in the somatosensory cortex showed significant apparent conductivity changes as variations in fMREIT phase (φ_d and ∇^2 φ_d) signals represented by fMREIT activation maps (F-tests, p <0.05). Consistent changes in the standard deviation of φ_d and ∇^2 φ_d in cortical voxels contralateral to forepaw stimulation were observed across imaging sessions. These preliminary findings show that fMREIT may have the potential to detect conductivity changes correlated with neural activity.
The development of fMREIT for the direct detection of neural activity using conductivity contrast in in vivo settings has been the focus of the research work presented here. An in vivo animal model was developed to detect neural activity initiated changes in neuronal membrane conductivities under external electrical current stimulation. Neural activity was induced in somatosensory areas I (SAI) and II (SAII) by applying electrical currents between the second and fourth digits of the rodent forepaw. The in vivo animal model involved the use of forepaw stimulation to evoke somatosensory neural activations along with hippocampal fMREIT imaging currents contemporaneously applied under magnetic field strengths of 7 Tesla. Three distinct types of fMREIT current waveforms were applied as imaging currents under two inhalants – air and carbogen. Active regions in the somatosensory cortex showed significant apparent conductivity changes as variations in fMREIT phase (φ_d and ∇^2 φ_d) signals represented by fMREIT activation maps (F-tests, p <0.05). Consistent changes in the standard deviation of φ_d and ∇^2 φ_d in cortical voxels contralateral to forepaw stimulation were observed across imaging sessions. These preliminary findings show that fMREIT may have the potential to detect conductivity changes correlated with neural activity.
ContributorsAshok Kumar, Neeta (Author) / Sadleir, Rosalind J (Thesis advisor) / Greger, Bradley (Committee member) / Muthuswamy, Jitendran (Committee member) / Tillery, Stephen H (Committee member) / Sohn, SungMin (Committee member) / Arizona State University (Publisher)
Created2020