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conference paper not in proceedings

Calibration-Free BCI Based Control

Grizou, Jonathan
•
Iturrate, Inaki  
•
Montesano, Luis
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2014
Twenty-Eighth AAAI Conference on Artificial Intelligence

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 interaction. 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.

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Type
conference paper not in proceedings
DOI
10.1609/aaai.v28i1.8923
Author(s)
Grizou, Jonathan
Iturrate, Inaki  
Montesano, Luis
Oudeyer, Pierre-Yves
Lopes, Manuel
Date Issued

2014

Subjects

Brain-Computer Interface

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CNBI  
CNP  
Event nameEvent placeEvent date
Twenty-Eighth AAAI Conference on Artificial Intelligence

Québec, Canada

2014

Available on Infoscience
May 14, 2014
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/103320
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