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dc.contributor.advisorMandayam A. Srinivasan.en_US
dc.contributor.authorKim, Hyun K., Ph. D. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.date.accessioned2006-11-07T12:00:34Z
dc.date.available2006-11-07T12:00:34Z
dc.date.copyright2005en_US
dc.date.issued2005en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/34411
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.en_US
dc.descriptionIncludes bibliographical references (p. 145-153).en_US
dc.description.abstractThe 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.en_US
dc.description.abstract(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.en_US
dc.description.statementofresponsibilityby Hyun K. Kim.en_US
dc.format.extent155 p.en_US
dc.format.extent8091424 bytes
dc.format.extent8100060 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectMechanical Engineering.en_US
dc.titleStrategies for control of neuroprostheses through Brain-Machine Interfacesen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.identifier.oclc70272109en_US


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