Bayesian learning in assisted brain-computer interface tasks - Robotics Institute Carnegie Mellon University

Bayesian learning in assisted brain-computer interface tasks

Yin Zhang, Andrew B. Schwartz, Steven Michael Chase, and Robert Kass
Conference Paper, Proceedings of 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '12), pp. 2740 - 2743, August, 2012

Abstract

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.

BibTeX

@conference{Zhang-2012-7571,
author = {Yin Zhang and Andrew B. Schwartz and Steven Michael Chase and Robert Kass},
title = {Bayesian learning in assisted brain-computer interface tasks},
booktitle = {Proceedings of 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '12)},
year = {2012},
month = {August},
pages = {2740 - 2743},
}