Brain-computer interfaces (BCIs) allow people to directly control devices with their thoughts. The BrainGate clinical trial is studying the use of intracortical BCIs in people with paralysis. Research participants in this trial have successfully used BCIs to control computer cursors, robotic arms, and other devices. A key component of this technology, called neural decoding, is the algorithm that translates the recorded brain signal into a movement command. In this talk I will describe some recent advances in neural decoding for BCIs, including a fast, learnable approximation for non-Gaussian filtering, that we call the discriminative Kalman filter. Bayesian methods play a prominent role. This is joint work with David Brandman, Michael Burkhart, and the BrainGate research team.
Matthew T. Harrison, Ph.D., is an Associate Professor of Applied Mathematics at Brown University in Providence, RI. His current research focuses on topics in statistics, machine learning, and data science, including statistical methods in neuroscience, conditional inference, multivariate binary time series and point processes, nonparametric Bayesian mixture models, importance sampling, and random graphs and tables. His research has been published in the leading journals of multiple disciplines, and he is an award-winning teacher at Brown. Dr. Harrison received his Ph.D. in Applied Mathematics from Brown University in 2005. Afterwards he was a postdoctoral member of the Mathematical Sciences Research Institute in Berkeley, CA, and a Visiting Assistant Professor of Statistics at Carnegie Mellon University in Pittsburgh, PA. He returned to Brown in 2009.
Refreshments: 3:30-4:00 (outside Baker 232M)