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Algorithms for Neural Prosthetic Applications


Abstract In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actua... (more)
Created Date 2017
Contributor Padmanaban, Subash (Author) / Greger, Bradley (Advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Crook, Sharon (Committee member) / Arizona State University (Publisher)
Subject Neurosciences / Biomedical engineering / Computer science / Brain machine interface / Feature selection / Machine learning / Neural prosthesis / Neuroscience / Peripheral nerve interface
Type Doctoral Dissertation
Extent 132 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Bioengineering 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis