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.


Published in:
Proceedings of 17th European Symposium on Artificial Neural Networks
Presented at:
17th European Symposium on Artificial Neural Networks, Bruges, Belgium, April, 22-24, 2009
Year:
2009
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 Record created 2010-01-26, last modified 2018-03-18

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