Date of Original Version




PubMed ID


Rights Management

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Abstract or Description

Successful implementation of a brain-computer interface depends critically on the subject's ability to learn how to modulate the neurons controlling the device. However, the subject's learning process is probably the least understood aspect of the control loop. How should training be adjusted to facilitate dexterous control of a prosthetic device? An effective training schedule should manipulate the difficulty of the task to provide enough information to guide improvement without overwhelming the subject. In this paper, we introduce a bayesian framework for modeling the closed-loop BCI learning process that treats the subject as a bandwidth-limited communication channel. We then develop an adaptive algorithm to find the optimal difficulty-schedule for performance improvement. Simulation results demonstrate that our algorithm yields faster learning rates than several other heuristic training schedules, and provides insight into the factors that might affect the learning process.





Published In

Proceedings of the IEEE Engineering in Medicine and Biology Society, 2012, 2740-2743.