Bayesian plan recognition for Brain-Computer Interfaces

Abstract

For people with very severe motor dysfunctions, Brain-Computer Interfaces (BCIs) may provide the solution to regain mobility and manipulation capabilities. Unfortunately, BCIs are characterized by a limited bandwidth and uncertainty on the BCI output. In the past, we have developed a Bayesian plan recognition framework that estimates from uncertain human-robot interface signals the task a robot should execute. This paper extends our plan recognition framework to incorporate uncertain BCI signals. A benchmark test is proposed and adopted to evaluate both the plan recognition framework and the performance of the BCI user, for the concrete application of wheelchair driving.