Degenhart, Alan D
(2015)
Evaluation and Advancement of Electrocorticographic Brain-Machine Interfaces for Individuals with Upper-Limb Paralysis.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
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Abstract
Brain-machine interface (BMI) technology aims to provide individuals with movement paralysis a natural and intuitive means for the restoration of function. Electrocorticography (ECoG), in which disc electrodes are placed on either the surface of the dura or the cortex to record field potential activity, has been proposed as a viable neural recording modality for BMI systems, potentially providing stable, long-term recordings of cortical activity with high spatial and temporal resolution. Previous demonstrations of BMI control using ECoG have consisted of short-term periods of control by able-bodied subjects utilizing basic processing and decoding techniques. This dissertation presents work seeking to advance the current state of ECoG BMIs through an assessment of the ability of individuals with movement paralysis to control an ECoG BMI, an investigation into adaptation during BMI skill acquisition, an evaluation of chronic implantation of an ECoG electrode grid, and improved extraction of BMI command signals from ECoG recordings.
Two individuals with upper-limb paralysis were implanted with high-density ECoG electrode grids over sensorimotor cortical areas for up to 30 days, with both subjects found to be capable of voluntarily modulating their cortical activity to control movement of a computer cursor with up to three degrees of freedom. Analysis of control signal angular error and the tuning characteristics of ECoG spectral features during the acquisition of brain control revealed that both decoder calibration and fixed-decoder training could facilitate performance improvements. In addition, to better understand the capability of ECoG to provide robust, long-term recordings, work was conducted assessing the effects of chronic implantation of an ECoG electrode grid in a non-human primate, demonstrating that movement-related modulation could be recorded from electrode nearly two years post-implantation despite the presence of substantial fibrotic encapsulation. Finally, it was found that the extraction of command signals from ECoG recordings could be improved through the use of a decoding method incorporating weight-space priors accounting for the expected correlation structure of electrical field potentials. Combined, this work both demonstrates the feasibility of ECoG-based BMI systems as well as addresses some of key challenges that must be overcome before such systems are translated to the clinical realm.
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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: |
28 January 2015 |
| Date Type: |
Publication |
| Defense Date: |
30 October 2014 |
| Approval Date: |
28 January 2015 |
| Submission Date: |
21 October 2014 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
210 |
| 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: |
Electrocorticography, ECoG, Brain-Machine Interface, BMI, Brain-Computer Interface, BCI , Spinal Cord Injury, Amyotrophic Lateral Sclerosis |
| Date Deposited: |
28 Jan 2015 16:55 |
| Last Modified: |
28 Jan 2017 06:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/23387 |
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