Neuroprosthetic decoder training as imitation learning

Understand

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.
• We describe how 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.