The self-paced Graz brain-computer interface: methods and applications

Authors:
Reinhold Scherer;Alois Schloegl;Felix Lee;Horst Bischof;Janez Janša;Gert Pfurtscheller
Affiliations:
Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria and NeuroCenter Styria, Graz, Austria;Intelligent Data Analysis Group, Fraunhofer-Institut für Rechnerarchitektur und Softwaretechnik, Berlin, Germany;Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria;Aksioma-Institute for Contemporary Art, Ljubljana, Slovenia;Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria and NeuroCenter Styria, Graz, Austria
Venue:
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Year:
2007

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Abstract

We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.