Bayesian plan recognition for brain-computer interfaces

Authors:
Eric Demeester;Alexander Hüntemann;José Del R. Millán;Hendrik Van Brussel
Affiliations:
Katholieke Universiteit Leuven, Department of Mechanical Engineering, Heverlee, Belgium;Katholieke Universiteit Leuven, Department of Mechanical Engineering, Heverlee, Belgium;Swiss Federal Institute of Technology, EPFL, Lausanne, Switzerland;Katholieke Universiteit Leuven, Department of Mechanical Engineering, Heverlee, Belgium
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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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.