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Poster
Playing Pinball with non-invasive BCI
Michael W Tangermann (ne Schröder) · Matthias Krauledat · Konrad Grzeska · Max Sagebaum · Benjamin Blankertz · Klaus-Robert Müller

Tue Dec 09 07:30 PM -- 12:00 AM (PST) @ None #None

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.

Author Information

Michael W Tangermann (ne Schröder) (Berlin Institute of Technology)
Matthias Krauledat (Technical University of Berlin)
Konrad Grzeska
Max Sagebaum
Benjamin Blankertz (Berlin Institute of Technology)
Klaus-Robert Müller (TU Berlin)

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