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- All Subjects: Neurosciences
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
Neural interfacing applications have advanced in complexity, with needs for increasingly high degrees of freedom in prosthetic device control, sharper discrimination in sensory percepts in bidirectional interfaces, and more precise localization of functional connectivity in the brain. As such, there is a growing need for reliable neurophysiological recordings at a fine spatial scale matching that of cortical columnar processing. Penetrating microelectrodes provide localization sufficient to isolate action potential (AP) waveforms, but often suffer from recorded signal deterioration linked to foreign body response. Micro-Electrocorticography (μECoG) surface electrodes elicit lower foreign body response and show greater chronic stability of recorded signals, though they typically lack the signal localization necessary to isolate individual APs. This dissertation validates the recording capacity of a novel, flexible, large area μECoG array with bilayer routing in a feline implant, and explores the ability of conventional μECoG arrays to detect features of neuronal activity in a very high frequency band associated with AP waveforms.
Recordings from both layers of the flexible μECoG array showed frequency features typical of cortical local field potentials (LFP) and were shown to be stable in amplitude over time. Recordings from both layers also showed consistent, frequency-dependent modulation after induction of general anesthesia, with large increases in beta and gamma band and decreases in theta band observed over three experiments. Recordings from conventional μECoG arrays over human cortex showed robust modulation in a high frequency (250-2000 Hz) band upon production of spoken words. Modulation in this band was used to predict spoken words with over 90% accuracy. Basal Ganglia neuronal AP firing was also shown to significantly correlate with various cortical μECoG recordings in this frequency band. Results indicate that μECoG surface electrodes may detect high frequency neuronal activity potentially associated with AP firing, a source of information previously unutilized by these devices.
Recordings from both layers of the flexible μECoG array showed frequency features typical of cortical local field potentials (LFP) and were shown to be stable in amplitude over time. Recordings from both layers also showed consistent, frequency-dependent modulation after induction of general anesthesia, with large increases in beta and gamma band and decreases in theta band observed over three experiments. Recordings from conventional μECoG arrays over human cortex showed robust modulation in a high frequency (250-2000 Hz) band upon production of spoken words. Modulation in this band was used to predict spoken words with over 90% accuracy. Basal Ganglia neuronal AP firing was also shown to significantly correlate with various cortical μECoG recordings in this frequency band. Results indicate that μECoG surface electrodes may detect high frequency neuronal activity potentially associated with AP firing, a source of information previously unutilized by these devices.
ContributorsBarton, Cody David (Author) / Greger, Bradley (Thesis advisor, Committee member) / Santello, Marco (Committee member) / Buneo, Christopher (Committee member) / Graudejus, Oliver (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2018
Description
To achieve the ambitious long-term goal of a feet of cooperating Flexible Autonomous
Machines operating in an uncertain Environment (FAME), this thesis addresses several
critical modeling, design, control objectives for rear-wheel drive ground vehicles.
Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform
that can be used for conducting FAME research.
A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities.
The augmented vehicle (FreeSLAM Robot) costs less than $500 but offers the capability
of commercially available vehicles costing over $2000.
All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following
summarizes the key hardware demonstrations presented and analyzed:
(1)Cruise (v, ) control along a line,
(2) Cruise (v, ) control along a curve,
(3) Planar (x, y) Cartesian Stabilization for rear wheel drive vehicle,
(4) Finish the track with camera pan tilt structure in minimum time,
(5) Finish the track without camera pan tilt structure in minimum time,
(6) Vision based tracking performance with different cruise speed vx,
(7) Vision based tracking performance with different camera fixed look-ahead distance L,
(8) Vision based tracking performance with different delay Td from vision subsystem,
(9) Manually remote controlled robot to perform indoor SLAM,
(10) Autonomously line guided robot to perform indoor SLAM.
For most cases, hardware data is compared with, and corroborated by, model based
simulation data. In short, the thesis uses low-cost self-designed rear-wheel
drive robot to demonstrate many capabilities that are critical in order to reach the
longer-term FAME goal.
Machines operating in an uncertain Environment (FAME), this thesis addresses several
critical modeling, design, control objectives for rear-wheel drive ground vehicles.
Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform
that can be used for conducting FAME research.
A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities.
The augmented vehicle (FreeSLAM Robot) costs less than $500 but offers the capability
of commercially available vehicles costing over $2000.
All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following
summarizes the key hardware demonstrations presented and analyzed:
(1)Cruise (v, ) control along a line,
(2) Cruise (v, ) control along a curve,
(3) Planar (x, y) Cartesian Stabilization for rear wheel drive vehicle,
(4) Finish the track with camera pan tilt structure in minimum time,
(5) Finish the track without camera pan tilt structure in minimum time,
(6) Vision based tracking performance with different cruise speed vx,
(7) Vision based tracking performance with different camera fixed look-ahead distance L,
(8) Vision based tracking performance with different delay Td from vision subsystem,
(9) Manually remote controlled robot to perform indoor SLAM,
(10) Autonomously line guided robot to perform indoor SLAM.
For most cases, hardware data is compared with, and corroborated by, model based
simulation data. In short, the thesis uses low-cost self-designed rear-wheel
drive robot to demonstrate many capabilities that are critical in order to reach the
longer-term FAME goal.
ContributorsLu, Xianglong (Author) / Rodriguez, Armando Antonio (Thesis advisor) / Berman, Spring (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2016
Description
A robotic swarm can be defined as a large group of inexpensive, interchangeable
robots with limited sensing and/or actuating capabilities that cooperate (explicitly
or implicitly) based on local communications and sensing in order to complete a
mission. Its inherent redundancy provides flexibility and robustness to failures and
environmental disturbances which guarantee the proper completion of the required
task. At the same time, human intuition and cognition can prove very useful in
extreme situations where a fast and reliable solution is needed. This idea led to the
creation of the field of Human-Swarm Interfaces (HSI) which attempts to incorporate
the human element into the control of robotic swarms for increased robustness and
reliability. The aim of the present work is to extend the current state-of-the-art in HSI
by applying ideas and principles from the field of Brain-Computer Interfaces (BCI),
which has proven to be very useful for people with motor disabilities. At first, a
preliminary investigation about the connection of brain activity and the observation
of swarm collective behaviors is conducted. After showing that such a connection
may exist, a hybrid BCI system is presented for the control of a swarm of quadrotors.
The system is based on the combination of motor imagery and the input from a game
controller, while its feasibility is proven through an extensive experimental process.
Finally, speech imagery is proposed as an alternative mental task for BCI applications.
This is done through a series of rigorous experiments and appropriate data analysis.
This work suggests that the integration of BCI principles in HSI applications can be
successful and it can potentially lead to systems that are more intuitive for the users
than the current state-of-the-art. At the same time, it motivates further research in
the area and sets the stepping stones for the potential development of the field of
Brain-Swarm Interfaces (BSI).
robots with limited sensing and/or actuating capabilities that cooperate (explicitly
or implicitly) based on local communications and sensing in order to complete a
mission. Its inherent redundancy provides flexibility and robustness to failures and
environmental disturbances which guarantee the proper completion of the required
task. At the same time, human intuition and cognition can prove very useful in
extreme situations where a fast and reliable solution is needed. This idea led to the
creation of the field of Human-Swarm Interfaces (HSI) which attempts to incorporate
the human element into the control of robotic swarms for increased robustness and
reliability. The aim of the present work is to extend the current state-of-the-art in HSI
by applying ideas and principles from the field of Brain-Computer Interfaces (BCI),
which has proven to be very useful for people with motor disabilities. At first, a
preliminary investigation about the connection of brain activity and the observation
of swarm collective behaviors is conducted. After showing that such a connection
may exist, a hybrid BCI system is presented for the control of a swarm of quadrotors.
The system is based on the combination of motor imagery and the input from a game
controller, while its feasibility is proven through an extensive experimental process.
Finally, speech imagery is proposed as an alternative mental task for BCI applications.
This is done through a series of rigorous experiments and appropriate data analysis.
This work suggests that the integration of BCI principles in HSI applications can be
successful and it can potentially lead to systems that are more intuitive for the users
than the current state-of-the-art. At the same time, it motivates further research in
the area and sets the stepping stones for the potential development of the field of
Brain-Swarm Interfaces (BSI).
ContributorsKaravas, Georgios Konstantinos (Author) / Artemiadis, Panagiotis (Thesis advisor) / Berman, Spring M. (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2017