Asynchronous BCI Control of a Robot Simulator with Supervised Online Training

  • Chun Sing Louis Tsui
  • John Q. Gan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)


Due to the non-stationarity of EEG signals, online training and adaptation is essential to EEG based brain-computer interface (BCI) systems. Asynchronous BCI offers more natural human-machine interaction, but it is a great challenge to train and adapt an asynchronous BCI online because the user’s control intention and timing are usually unknown. This paper proposes a novel motor imagery based asynchronous BCI for controlling a simulated robot in a specifically designed environment which is able to provide user’s control intention and timing during online experiments, so that online training and adaptation of motor imagery based asynchronous BCI can be effectively investigated. This paper also proposes an online training method, attempting to automate the process of finding the optimal parameter values of the BCI system to deal with non-stationary EEG signals. Experimental results have shown that the proposed methodfor online training of asynchronous BCI significantly improves the performance.


Adaptation asynchronous BCI brain-computer interface online training automated learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Chun Sing Louis Tsui
    • 1
  • John Q. Gan
    • 1
  1. 1.BCI Group, Department of Computer Science, University of Essex, Colchester, CO4 3SQUnited Kingdom

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