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.


Publié dans:
Proceedings of 17th European Symposium on Artificial Neural Networks
Présenté à:
17th European Symposium on Artificial Neural Networks, Bruges, Belgium, April, 22-24, 2009
Année
2009
Mots-clefs:
Laboratoires:




 Notice créée le 2010-01-26, modifiée le 2019-03-16

Lien externe:
Télécharger le document
URL
Évaluer ce document:

Rate this document:
1
2
3
 
(Pas encore évalué)