Neuroprosthetic Decoder Training as Imitation Learning

Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham

Abstract: Author Summary There are various existing methods for rapidly learning a decoder during closed-loop brain computer interface (BCI) tasks. While many of these methods work well in practice, there is no clear theoretical foundation for parameter learning. We offer a unification of closed-loop decoder learning setting as an imitation learning problem. This has two major consequences: first, our approach clarifies how to derive “intention-based” algorithms for any BCI setting, most notably more complex settings like control of an arm; and second, this framework allows us to provide theoretical results, building from an existing literature on the regret of related algorithms. After first demonstrating algorithmic performance in simulation on the well-studied setting of a user trying to reach targets by controlling a cursor on a screen, we then simulate a user controlling an arm with many degrees of freedom in order to grasp a wand. Finally, we describe how extensions in the online-imitation learning literature can improve BCI in additional settings.