Measurements and technology for long-term neural prosthetic systems
Abstract/Contents
- Abstract
- Cortical brain-machine interfaces, or BMIs, is a relatively new field with the potential to provide many different clinical treatments, particularly for fully paralyzed patients. In these applications, multichannel electrode arrays are implanted into motor cortical areas in order to extract useful control signals. My research focuses on taking proof-of-concept academic BMI systems, and solving the engineering challenges that currently prevent them from being used in a clinical setting. These challenges include running a BMI for more than a few hours or a single day, and finding ways to minimize the size, cost, and operational complexity of the complete system. This dissertation includes an analysis of neuron stability over long timescales. I will show that the relationship between neurons in motor cortices and behavior remains stationary over time despite substantial noise, which could mitigate some concerns about long-term BMI performance. I will also discuss the development of HermesC, a wireless system for recording multichannel neural data from freely moving primates. This device dramatically reduces the size and cost of current recording technology for real-time neural prosthetic systems, and could be useful for human clinical trials. It may also enable neural prosthetic studies with animals in a less constrained setting. Combining traditional neural recordings with overnight wireless neural recordings, I will also show that there are substantial changes in neural waveforms from single neurons across days. However, the quality of neural decodes (the extraction of useful control signals) is only slightly improved by sorting individual units rather than using simple threshold crossings. This may enable long term BMI operation because multiunit neural "hash" on electrode arrays tends to persist for a long time, perhaps years, after single neuron signals have declined due to various tissue responses. In fact, other recent work from this project has demonstrated high performance neural decodes using only threshold crossings on arrays ~2.5 years after implantation.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2010 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Chestek, Cynthia Anne | |
|---|---|---|
| Associated with | Stanford University, Department of Electrical Engineering | |
| Primary advisor | Meng, Teresa H | |
| Primary advisor | Shenoy, Krishna V. (Krishna Vaughn) | |
| Thesis advisor | Meng, Teresa H | |
| Thesis advisor | Shenoy, Krishna V. (Krishna Vaughn) | |
| Thesis advisor | Murmann, Boris | |
| Thesis advisor | Newsome, William T | |
| Advisor | Murmann, Boris | |
| Advisor | Newsome, William T |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Cynthia Anne Chestek. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph. D.)--Stanford University, 2010. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2010 by Cynthia Anne Chestek
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