Optimal Achievable Encoding for Brain Machine Interface
Technical Report,01 Oct 2016,30 Sep 2017
Leland Stanford Junior University, The Stanford United States
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Our goal was to develop efficient algorithms for encoding visual stimuli using an artificial retina, in a manner that optimizes the artificial visual image delivered to the brain. We did this by focusing on three components required. First, we developed novel models of retinal encoding that improve upon the state of the art, by using machine learning methods to incorporate spatial and temporal nonlinearities. Second, we developed novel methods of decoding images from retinal responses, using artificial neural networks, and by applying linear decoding to complete recorded populations of retinal ganglion cells for the first time. Third, we developed a greedy, dictionary-based encoding approach to translate a visual image into sequential patterns of electrical stimulation in real time, in a manner that optimizes visual image transfer to the brain.
- Human Factors Engineering and Man Machine Systems