Cognitive Neural Prosthetics: Brain Machine Interfaces Based in Parietal Cortex

Citation

Meeker, Daniella Elena Patricia (2005) Cognitive Neural Prosthetics: Brain Machine Interfaces Based in Parietal Cortex. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/V2BZ-EG30. https://resolver.caltech.edu/CaltechETD:etd-06032005-170438

Abstract

Systems neuroscience has recently emerged as an applied field in the form of neural prosthetic development. This integration of empirical systems neuroscience with engineering in order to develop functional interfaces between external devices and the brain has not only been beneficial in its applied goal, but has resulted in observations of scientific interest. The body of work presented here demonstrates the efficacy of two varieties of brain machine interfaces (BMIs) based in Parietal Cortex. The first using information about intended reaches present in action potentials, the second using local field potentials (LFPs). Both studies were predicated and succeeded with offline analyses demonstrating feasibility and novel insight to the function and neural coding properties of Parietal Cortex. We found that using BMIs resulted in adaptive change which tended to improve performance. LFPs, though less successful than spikes for BMI control under these experimental conditions, appear to have a multiplexing of different types of information that might aid in BMIs as well as providing a different way of looking at the neural processing. A preliminary exploration of relative timing of spikes and LFPs might result in some of the adaptive properties observed during BMI use via spike timing dependent plasticity concludes the research presented here.

Thesis Files