School of Medicine
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Postdoctoral Research Fellow, Neurosurgery
Current Research and Scholarly Interests I'm trying to restore movement and communication -- and thus, independence -- to people with paralysis. To do so, I'm discovering ways to use neurotechnology to allow people to communicate far more information from their brain to the outside world.
One branch of my brain-computer interface research is to read out complex (high degree-of-freedom) arm movement commands from motor areas of cortex, so that patients can make dextrous movements with, for example, a robotic arm. The goal is to provide enough range of movement that people can perform essential activities of daily living and take care of themselves.
A second branch is to build speech brain-computer interfaces by decoding the neural signals associated with trying to talk. More specifically, I'm trying to reconstruct speech from the movement commands that the brain would normally send to the lips, tongue, jaw, etc. Most work in this space has been using electrocorticography, whereas I'm using electrodes that go into the brain, where we can potentially access more information thanks to the ability to detect individual neurons' action potentials.
My Ph.D. research spanned both fundamental motor neuroscience and applied neural engineering. On the basic science side, I investigated 1) how "internal models" of how the brain's output effects the arm are used by motor cortex. 2) how sensory information carrying information about movement errors is prevented from interfering with motor cortical output until it is "ready" to generate the appropriate output; and 3) how the dynamical rules governing motor cortical activity restrict the kinds of outputs it can generate for the purpose of commanding a neural prosthesis.
My Ph.D. neural engineering work focused on 1) how to robustly decode a user's intended movement despite minute-by-minute and day-to-day changes in neural signals, and 2) sensors' gradually losing the ability to record neuronal action potential. I also studied (3) the effect of ongoing sensory feedback on this signal, and how we can exploit this information to detect and automatically correct for errors.