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    Team PhyPA works on the implementation of Brain-Computer Interface (BCI) Technology into general Human-Machine Systems, not being focused on medical applications.

    A widely accepted definition of a BCI system is to “give their users communication and control channels that do not depend on the brain’s normal output channels of peripheral nerves and muscles” (Wolpaw et al. 2002). Team PhyPA shifts this perspective from the user to the application itself which allows for a widened definition of a BCI:

    A BCI is a system to provide computer applications with access to real-time information about cognitive state, on the basis of measured brain activity.

    Based on that definition we have introduced a categorization of BCI approaches (Zander et al. 2008):

    Active BCI: An active BCI is a BCI which derives its outputs from brain activity which is consciously controlled by the user. Such a system is independent from external events, for controlling an application.

    Reactive BCI: A reactive BCI is a BCI which derives its outputs from brain activity arising in reaction to external stimulation, which is indirectly modulated by the user for controlling an application.

    Passive BCI: A passive BCI is a BCI which derives its outputs from arbitrary brain activity without the purpose of voluntary control, for enriching a human-computer interaction with implicit information.

     

    Passive BCIs are especially suitable for implicit interaction in Human-Machine Systems, detecting covert aspects of the user state: A Covert Aspect of User State (CAUS) is a process occurring within the user which can only be detected with weak reliability by using overt measures, like behaviour.

    Recently, we have implemented passive BCI systems for the detection of CAUS like the interpretation of errors within the interaction, the perceived loss of control over a system, and the interpretation of observed human movements or bluffing in a game context.

    Additionlay, Team PhyPA works on the application of hybrid BCIs (Pfurtscheller et al. 2006) – like solving the MIDAS-Touch problem by combining gaze control with different types of BCIs (Vilimek and Zander, 2009).

    References



    Pfurtscheller G., Allison, Bauernfeind, Brunner, ... , Zander, Neuper, Birbaumer The Hybrid BCI Frontiers in Neuroprosthetics, 2:3. doi: 10.3389/fnpro.2010.00003, 2010

    Vilimek R. & Zander T.O. BC(eye): Combining Eye-Gaze Input with Brain-Computer Interaction. In Proceedings of the HCII 2009. Heidelberg, Germany: Springerlink, 2009.

    T. O. Zander, C. Kothe, S. Welke, and M. Roetting. Enhancing human-machine systems with secondary input from passive brain-computer interfaces. In Proc. of the 4th Int. BCI Workshop & Training Course, Graz University of Technology Publishing House, 2008, Graz, Austria.

    Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Vaughan T.M. Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6):767-91, 2002.