Decoder algorithm design for high-performance and robust neural prostheses
Abstract/Contents
- Abstract
- Millions of people suffer from motor-related neurological injury or disease, which in some cases is so severe that even the ability to communicate is lost. For people with lost motor function, including paralysis and amyotrophic lateral sclerosis, neural prostheses are an emerging technology that has the potential to increase quality of life and enable greater communication with the world. Neural prostheses record neural signals from the brain and, through a decoder algorithm, translates these neural signals into control signals for actuating machines such as a computer cursor or a prosthetic arm. There are three important areas of development for neural prostheses to be clinically viable. Specifically, these systems must be (1) high-performance, (2) robust to perturbations and noise, and (3) usable for decades. My dissertation is focused on the design of decoder algorithms that achieve state-of-the-art performance in all three of these areas. These decoder algorithms rely on techniques and intuition from machine learning, statistical signal processing, and neuroscience. Together, these advances in decode algorithm design increase the clinical viability of neural prostheses.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Kao, Jonathan C |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Shenoy, Krishna V. (Krishna Vaughn) |
| Thesis advisor | Shenoy, Krishna V. (Krishna Vaughn) |
| Thesis advisor | Goldsmith, Andrea, 1964- |
| Thesis advisor | Henderson, Jaimie (Jaimie M.) |
| Advisor | Goldsmith, Andrea, 1964- |
| Advisor | Henderson, Jaimie (Jaimie M.) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jonathan C. Kao. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Jonathan Chau-Yan Kao
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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