Augmenting Information from Brain-Computer Interfaces through Bayesian Plan Recognition

For severely disabled people, Brain-Computer Interfaces (BCIs) may provide the means to regain mobility and manipulation capabilities. However, information obtained from current BCIs is uncertain and of limited bandwidth and resolution. This paper presents a Bayesian framework that estimates from uncertain BCI signals a richer representation of the task a robotic mobility or manipulation device should execute, such that these devices can be operated more safely, accurately and efficiently. The framework has been evaluated on a simulated robotic wheelchair.