Michael Tangermann (ne Schröder), Matthias Krauledat, Konrad Grzeska, Max Sagebaum, Benjamin Blankertz, Klaus-Robert Muller

Berlin Institute of Technology; Technical University of Berlin; ; ; Berlin Institute of Technology; TU Berlin

Playing Pinball with non-invasive BCI

7:30pm - 12:00am Tuesday, December 09, 2008

This is part of the Posters which begins at 19:30 on Tuesday December 9, 2008

T66

Compared to invasive Brain-Computer Interfaces (BCI), non-invasive BCI systems based on Electroencephalogram (EEG) signals have not been applied successfully for complex control tasks. In the present study, however, we demonstrate this is possible and report on the interaction of a human subject with a complex real device: a pinball machine. First results in this single subject study clearly show that fast and well-timed control well beyond chance level is possible, even though the environment is extremely rich and requires complex predictive behavior. Using machine learning methods for mental state decoding, BCI-based pinball control is possible within the first session without the necessity to employ lengthy subject training. While the current study is still of anecdotal nature, it clearly shows that very compelling control with excellent timing and dynamics is possible for a non-invasive BCI.