Strategies for control of neuroprostheses through Brain-Machine Interfaces
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2006
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| Online Access: | http://hdl.handle.net/1721.1/34411 |
| _version_ | 1826199979208736768 |
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| author | Kim, Hyun K., Ph. D. Massachusetts Institute of Technology |
| author2 | Mandayam A. Srinivasan. |
| author_facet | Mandayam A. Srinivasan. Kim, Hyun K., Ph. D. Massachusetts Institute of Technology |
| author_sort | Kim, Hyun K., Ph. D. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005. |
| first_indexed | 2024-09-23T11:28:50Z |
| format | Thesis |
| id | mit-1721.1/34411 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:28:50Z |
| publishDate | 2006 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/344112019-04-12T10:00:35Z Strategies for control of neuroprostheses through Brain-Machine Interfaces Kim, Hyun K., Ph. D. Massachusetts Institute of Technology Mandayam A. Srinivasan. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Mechanical Engineering. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005. Includes bibliographical references (p. 145-153). The concept of brain controlled machines sparks our imagination with many exciting possibilities. One potential application is in neuroprostheses for paralyzed patients or amputees. The quality of life of those who have extremely limited motor abilities can potentially be improved if we have a means of inferring their motor intent from neural signals and commanding a robotic device that can be controlled to perform as a smart prosthesis. In our recent demonstration of such Brain Machine Interfaces (BMIs) monkeys were able to control a robot arm in 3-D motion directly, due to advances in accessing, recording, and decoding electrical activity of populations of single neurons in the brain, together with algorithms for driving robotic devices with the decoded neural signals in real time. However, such demonstrations of BMI thus far have been limited to simple position control of graphical cursors or robots in free space with non-human primates. There still remain many challenges in reducing this technology to practice in a neuroprosthesis for humans. The research in this thesis introduces strategies for optimizing the information extracted from the recorded neural signals, so that a practically viable and ultimately useful neuroprosthesis can be achieved. A framework for incorporating robot sensors and reflex like behavior has been introduced in the form of Continuous Shared Control. The strategy provides means for more steady and natural movement by compensating for the natural reflexes that are absent in direct brain control. The Muscle Activation Method, an alternative decoding algorithm for extracting motor parameters from the neural activity, has been presented. (cont.) The method allows the prosthesis to be controlled under impedance control, which is similar to how our natural limbs are controlled. Using this method, the prosthesis can perform a much wider range in of tasks in partially known and unknown environments. Finally preparations have been made for clinical trials with humans, which would signify a major step in reaching the ultimate goal of human brain operated machines. by Hyun K. Kim. Ph.D. 2006-11-07T12:00:34Z 2006-11-07T12:00:34Z 2005 2005 Thesis http://hdl.handle.net/1721.1/34411 70272109 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 155 p. 8091424 bytes 8100060 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mechanical Engineering. Kim, Hyun K., Ph. D. Massachusetts Institute of Technology Strategies for control of neuroprostheses through Brain-Machine Interfaces |
| title | Strategies for control of neuroprostheses through Brain-Machine Interfaces |
| title_full | Strategies for control of neuroprostheses through Brain-Machine Interfaces |
| title_fullStr | Strategies for control of neuroprostheses through Brain-Machine Interfaces |
| title_full_unstemmed | Strategies for control of neuroprostheses through Brain-Machine Interfaces |
| title_short | Strategies for control of neuroprostheses through Brain-Machine Interfaces |
| title_sort | strategies for control of neuroprostheses through brain machine interfaces |
| topic | Mechanical Engineering. |
| url | http://hdl.handle.net/1721.1/34411 |
| work_keys_str_mv | AT kimhyunkphdmassachusettsinstituteoftechnology strategiesforcontrolofneuroprosthesesthroughbrainmachineinterfaces |