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.
Type
Journal articleSubject
AlgorithmsArm
Brain-Computer Interfaces
Computational Biology
Computer Simulation
Humans
Learning
Robotics
Supervised Machine Learning
Task Performance and Analysis
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https://hdl.handle.net/10161/16068Published Version (Please cite this version)
10.1371/journal.pcbi.1004948Publication Info
Carlson, D; Cunningham, JP; Merel, J; & Paninski, Liam (2018). Neuroprosthetic Decoder Training as Imitation Learning. PLoS Comput Biol, 12(5). pp. e1004948. 10.1371/journal.pcbi.1004948. Retrieved from https://hdl.handle.net/10161/16068.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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