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Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button
Joan Fruitet · Alexandra Carpentier · Remi Munos · Maureen Clerc

Thu Dec 06 02:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

A brain-computer interface (BCI) allows users to “communicate” with a computer without using their muscles. BCI based on sensori-motor rhythms use imaginary motor tasks, such as moving the right or left hand to send control signals. The performances of a BCI can vary greatly across users but also depend on the tasks used, making the problem of appropriate task selection an important issue. This study presents a new procedure to automatically select as fast as possible a discriminant motor task for a brain-controlled button. We develop for this purpose an adaptive algorithm UCB-classif based on the stochastic bandit theory. This shortens the training stage, thereby allowing the exploration of a greater variety of tasks. 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. Comparing the proposed method to the standard practice in task selection, for a fixed time budget, UCB-classif leads to an improve classification rate, and for a fix classification rate, to a reduction of the time spent in training by 50%.

Author Information

Joan Fruitet (INRIA)
Alexandra Carpentier (StatsLab Cambridge)
Remi Munos (Google DeepMind)
Maureen Clerc (INRIA)

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