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
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 : Objective. Brain-computer interfaces (BCIs) based on sensorimotor rhythms use a variety of motor tasks, such as imagining moving the right or left hand, the feet or the tongue. Finding the tasks that yield best performance, specifically to each user, is a time-consuming preliminary phase to a BCI experiment. This study presents a new adaptive procedure to automatically select (online) the most promising motor task for an asynchronous brain-controlled button. Approach. We develop for this purpose an adaptive algorithm UCB-classif based on the stochastic bandit theory and design an EEG experiment to test our method. We compare (offline) the adaptive algorithm to a naïve selection strategy which uses uniformly distributed samples from each task. We also run the adaptive algorithm online to fully validate the approach. Main results. By not wasting time on inefficient tasks, and focusing on the most promising ones, this algorithm results in a faster task selection and a more efficient use of the BCI training session. More precisely, the offline analysis reveals that the use of this algorithm can reduce the time needed to select the most appropriate task by almost half without loss in precision, or alternatively, allow us to investigate twice the number of tasks within a similar time span. Online tests confirm that the method leads to an optimal task selection. Significance. This study is the first one to optimize the task selection phase by an adaptive procedure. By increasing the number of tasks that can be tested in a given time span, the proposed method could contribute to reducing 'BCI illiteracy'.
Type de document :
Article dans une revue
Journal of Neural Engineering, IOP Publishing, 2013, 10 (1), 〈10.1088/1741-2560/10/1/016012〉
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https://hal.inria.fr/hal-00798561
Contributeur : Maureen Clerc <>
Soumis le : vendredi 8 mars 2013 - 17:32:43
Dernière modification le : vendredi 12 janvier 2018 - 01:48:29

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Joan Fruitet, Alexandra Carpentier, Rémi Munos, Maureen Clerc. Automatic motor task selection via a bandit algorithm for a brain-controlled button. Journal of Neural Engineering, IOP Publishing, 2013, 10 (1), 〈10.1088/1741-2560/10/1/016012〉. 〈hal-00798561〉

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