title: Neuroprosthetic Decoder Training as Imitation Learning.
abstract: Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user's intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector.
authors.affiliation: Neurobiology and Behavior program, Columbia University, New York, New York, United States of America.|Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America.
authors.forename: Josh
authors.initials: J
authors.lastname: Merel
authors.affiliation: Department of Statistics, Columbia University, New York, New York, United States of America.|Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America.
authors.forename: David
authors.identifier: 0000-0003-1005-6385
authors.initials: D
authors.lastname: Carlson
authors.affiliation: Neurobiology and Behavior program, Columbia University, New York, New York, United States of America.|Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America.|Department of Statistics, Columbia University, New York, New York, United States of America.|Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America.
authors.forename: Liam
authors.initials: L
authors.lastname: Paninski
authors.affiliation: Neurobiology and Behavior program, Columbia University, New York, New York, United States of America.|Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America.|Department of Statistics, Columbia University, New York, New York, United States of America.|Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America.
authors.forename: John P
authors.initials: JP
authors.lastname: Cunningham
mesh_terms: D000465:Algorithms; D001132:Arm; D062207:Brain-Computer Interfaces / Q000706:statistics & numerical data*; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D007858:Learning; D012371:Robotics; D000069553:Supervised Machine Learning; D013647:Task Performance and Analysis