Downey, John E.
(2017)
Stability of recording and neural tuning during intracortical brain-computer interface arm control.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
For intracortical brain-computer interface (BCI) controlled neuroprosthetic arms to become a valuable assistive technology for people with upper-limb paralysis they will need to be able to adjust to a number of changes in neural activity that have not previously been well characterized. These include recording instabilities, and changes in neural tuning when interacting with objects. Here I present characterizations of these problems in two human subjects, along with a few solutions.
I quantified the rate at which recorded units become unstable within and between days to inform the design of self-recalibrating decoders. These decoders will provide BCI users with consistent performance, even as units become unstable, by updating to incorporate new units before too many original units have become unstable.
Using the quantification of stability, I also examined whether unit characteristics could predict how long a unit would be stable. I found that units with high firing rates, large peak-to-peak voltages, and more accurate tuning were most likely to remain stable. Using this result, future work should be able to create decoders that preferentially rely on stable units in order to enable high-performance BCI control for longer.
I addressed difficulties that the first subject was having using the hand to interact with objects. I identified the source of the problem as an increased firing rate across much of the population when the hand approached objects. I then developed a method to remove the increase in firing rate before decoding so that the arm kinematics became predictable when the hand approached objects.
Finally, I studied the representation of desired grasp force in primary motor cortex to enable BCI users to grasp a variety of objects, from light, fragile objects to heavy, sturdy objects. I found that primary motor cortex represents grasp force in a predictable manner during grasping, but that the tuning to grasp force is not apparent while the user carries an object. These results will enable the creation of BCI decoders that can apply the appropriate amount of force when grasping, and avoid dropping objects while transporting them.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
26 September 2017 |
| Date Type: |
Publication |
| Defense Date: |
28 March 2017 |
| Approval Date: |
26 September 2017 |
| Submission Date: |
28 March 2017 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
156 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Bioengineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Brain-computer interface, neuroprosthetic, spinal cord injury, electrophysiology, robotic arm |
| Date Deposited: |
26 Sep 2017 16:29 |
| Last Modified: |
26 Sep 2019 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/31071 |
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