Author manuscript, published in "IROS 2013 Workshop on Neuroscience and Robotics (2013)" Zero-calibration BMIs for sequential tasks using error-related potentials (2013)
BibTeX
@MISC{Grizou13authormanuscript,,
author = {Jonathan Grizou and Iñaki Iturrate and Luis Montesano and Manuel Lopes and Pierre-yves Oudeyer},
title = {Author manuscript, published in "IROS 2013 Workshop on Neuroscience and Robotics (2013)" Zero-calibration BMIs for sequential tasks using error-related potentials},
year = {2013}
}
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Abstract
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. I.
Keyphrases
error-related potential author manuscript zero-calibration bmi sequential task human user online experiment present empirical result possible signal meaning brain computer interface signal decoder brain signal practical interaction human robot interaction brain machine interface realistic condition present result possible task
