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Zero-calibration BMIs for sequential tasks using error-related potentials

Abstract : Do we need to explicitly calibrate Brain Machine Interfaces (BMIs)? Can we start controlling a device without telling this device how to interpret brain signals? Can we learn how to communicate with a human user through practical interaction? It sounds like an ill posed problem, how can we control a device if such device does not know what our signals mean? This paper argues and present empirical results showing that, under specific but realistic conditions, this problem can be solved. We show that a signal decoder can be learnt automatically and online by the system under the assumption that both, human and machine, share the same a priori on the possible signals' meanings and the possible tasks the user may want the device to achieve. We present results from online experiments on a Brain Computer Interface (BCI) and a Human Robot Interaction (HRI) scenario.
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Contributor : Jonathan Grizou Connect in order to contact the contributor
Submitted on : Sunday, October 13, 2013 - 4:34:05 PM
Last modification on : Friday, January 21, 2022 - 3:08:51 AM
Long-term archiving on: : Friday, April 7, 2017 - 10:10:42 AM


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  • HAL Id : hal-00872484, version 1



Jonathan Grizou, Iñaki Iturrate, Luis Montesano, Manuel Lopes, Pierre-Yves Oudeyer. Zero-calibration BMIs for sequential tasks using error-related potentials. IROS 2013 Workshop on Neuroscience and Robotics, Nov 2013, Tokyo, Japan. ⟨hal-00872484⟩



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