A stochastic control framework for the design of observational brain-computer interfaces based on human error potentials

Brain-computer interface;feedback-related negativity;Bayesian control rule

Steines, David A. ; Coleman ; Todd P.

关键词: Brain-computer interface;    feedback-related negativity;    Bayesian control rule;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/24378/Steines_David.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Brain-computer interfaces (BCI) allow human subjects to interact with exogenous systems through neural signals. The goal of this interaction may be to induce behaviors or properties in either the exogenous system or thehuman subject. In this thesis, we develop a novel framework for designing BCIs based on principles from adaptive control. In particular, we exploit scalp electroencephalography-derived correlates of the human error processing system to recover a subject’s desired policy for the exogenous system. This scheme allows a human subject to control a system through passive observation by critiquing actions taken by the system. We provide a necessaryand sufficient condition for convergence and simulations as a proof of concept. Further, we discuss the application of this framework to building co-adaptive BCIs and as a tool for understanding the learning process during BCI interaction.