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Comparison of Feature Selection Methods for Robust Dexterous Decoding of Finger Movements from the Primary Motor Cortex of a Non-human Primate Using Support Vector Machine

Abstract Robust and stable decoding of neural signals is imperative for implementing a useful neuroprosthesis capable of carrying out dexterous tasks. A nonhuman primate (NHP) was trained to perform combined flexions of the thumb, index and middle fingers in addition to individual flexions and extensions of the same digits. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon action potential firing rates. The effect of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis, and Mutual Informat... (more)
Created Date 2015
Contributor Padmanaban, Subash (Author) / Greger, Bradley (Advisor) / Santello, Marco (Advisor) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Subject Biomedical engineering / Feature selection / Machine learning / neural decoding / Neural engineering / Neuroprosthetics / Support vector machine
Type Masters Thesis
Extent 43 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Bioengineering 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis