Calibration-Free BCI Based Control

Abstract : Recent works have explored the use of brain signals to directly control virtual and robotic agents in sequential tasks. So far in such brain-computer interfaces (BCI), an explicit calibration phase was required to build a decoder that translates raw electroencephalography (EEG) signals from the brain of each user into meaningful instructions. This paper proposes a method that removes the calibration phase, and allows a user to control an agent to solve a sequential task. The proposed method assumes a distribution of possible tasks, and infers the interpretation of EEG signals and the task by selecting the hypothesis which best explains the history of inter- action. We introduce a measure of uncertainty on the task and on the EEG signal interpretation to act as an exploratory bonus for a planning strategy. This speeds up learning by guiding the system to regions that better disambiguate among task hypotheses. We report experiments where four users use BCI to control an agent on a virtual world to reach a target without any previous calibration process.
Type de document :
Communication dans un congrès
Twenty-Eighth AAAI Conference on Artificial Intelligence, Jul 2014, Quebec, Canada. pp.1-8, 2014
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Contributeur : Jonathan Grizou <>
Soumis le : dimanche 27 avril 2014 - 13:14:51
Dernière modification le : jeudi 14 février 2019 - 15:33:33
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  • HAL Id : hal-00984068, version 1


Jonathan Grizou, Iñaki Iturrate, Luis Montesano, Pierre-Yves Oudeyer, Manuel Lopes. Calibration-Free BCI Based Control. Twenty-Eighth AAAI Conference on Artificial Intelligence, Jul 2014, Quebec, Canada. pp.1-8, 2014. 〈hal-00984068〉



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