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Neural Correlates of Learning in Brain Machine Interface Controlled Tasks


Abstract Brain-machine interfaces (BMIs) were first imagined as a technology that would allow subjects to have direct communication with prosthetics and external devices (e.g. control over a computer cursor or robotic arm movement). Operation of these devices was not automatic, and subjects needed calibration and training in order to master this control. In short, learning became a key component in controlling these systems. As a result, BMIs have become ideal tools to probe and explore brain activity, since they allow the isolation of neural inputs and systematic altering of the relationships between the neural signals and output. I have used BMIs to explore the process of brain adaptability in a motor-like task. To this end, I trained non-human pr... (more)
Created Date 2015
Contributor Armenta Salas, Michelle (Author) / Helms Tillery, Stephen I (Advisor) / Si, Jennie (Committee member) / Buneo, Christopher (Committee member) / Santello, Marco (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Subject Biomedical engineering / Neurosciences / brain-machine interfaces / motor adaptation / Motor learning / Neuroengineering / Neuroprosthetics
Type Doctoral Dissertation
Extent 136 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Bioengineering 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis