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Automatic motor task selection via a bandit algorithm for a brain-controlled button

Joan Fruitet 1 Alexandra Carpentier 2 Rémi Munos 2 Maureen Clerc 1, * 
* Corresponding author
1 ATHENA - Computational Imaging of the Central Nervous System
CRISAM - Inria Sophia Antipolis - Méditerranée
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : This study presents a new procedure to automatically select a discriminant motor task for an asynchronous brain-controlled button. This type of control pertains to Brain Computer Interfaces (BCI). When using sensorimotor rythms in a BCI, several motor tasks, such as moving the right or left hand, the feet or the tongue, can be considered as candidates for the control. This report presents a method to select as fast as possible the most promising task. We develop for this purpose an adaptive algorithm UCB-classif based on the stochastic bandit theory and build an EEG experiment to test our method. By not wasting time on ineffi cient tasks, our algorithm can focus on the most promising ones, resulting in a faster task selection and a more e cient use of the BCI training session. This leads to better classi cation rates for a xed time budget, compared to a standard task selection.
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Submitted on : Tuesday, September 20, 2011 - 10:28:20 AM
Last modification on : Saturday, June 25, 2022 - 11:06:43 PM
Long-term archiving on: : Tuesday, November 13, 2012 - 2:00:38 PM


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  • HAL Id : inria-00624686, version 1



Joan Fruitet, Alexandra Carpentier, Rémi Munos, Maureen Clerc. Automatic motor task selection via a bandit algorithm for a brain-controlled button. [Rapport de recherche] RR-7721, INRIA. 2011. ⟨inria-00624686⟩



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