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

Fruitet, Joan; Carpentier, Alexandra; Munos, Rémi; Clerc, Maureen

Document type :

Article dans une revue scientifique: Article original

DOI :

10.1088/1741-2560/10/1/016012

Title :

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

Author(s) :

Fruitet, Joan [Auteur]
Computational Imaging of the Central Nervous System [ATHENA]
Carpentier, Alexandra [Auteur]
Sequential Learning [SEQUEL]
Munos, Rémi [Auteur]
Sequential Learning [SEQUEL]
Clerc, Maureen [Auteur]
Computational Imaging of the Central Nervous System [ATHENA]

Journal title :

Journal of Neural Engineering

Publisher :

IOP Publishing

Publication date :

2013-01-21

ISSN :

1741-2560

HAL domain(s) :

Informatique [cs]/Apprentissage [cs.LG]

English abstract : [en]

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, ...
Show more >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'.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Files

https://hal.inria.fr/inria-00624686/file/RR-7721.pdf
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