Playing pinball with non-invasive BCI

Michael W. Tangermann; Matthias Krauledat; Konrad Grzeska; Max Sagebaum; Carmen Vidaurre; Benjamin Blankertz; Klaus Robert Müller

Playing pinball with non-invasive BCI

Michael W. Tangermann, Matthias Krauledat, Konrad Grzeska, Max Sagebaum, Carmen Vidaurre, Benjamin Blankertz, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

94 Citations (Scopus)

Abstract

Compared to invasive Brain-Computer Interfaces (BCI), non-invasive BCI systems based on Electroencephalogram (EEG) signals have not been applied successfully for precisely timed control tasks. In the present study, however, we demonstrate and report on the interaction of subjects with a real device: a pinball machine. Results of this study clearly show that fast and well-timed control well beyond chance level is possible, even though the environment is extremely rich and requires precisely timed and 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. The current study shows clearly that very compelling control with excellent timing and dynamics is possible for a non-invasive BCI.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Pages	1641-1648
Number of pages	8
Publication status	Published - 2009
Externally published	Yes
Event	22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada Duration: 2008 Dec 8 → 2008 Dec 11

Publication series

Name	Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Other

Other	22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
Country/Territory	Canada
City	Vancouver, BC
Period	08/12/8 → 08/12/11

ASJC Scopus subject areas

Information Systems

Cite this

Playing pinball with non-invasive BCI. / Tangermann, Michael W.; Krauledat, Matthias; Grzeska, Konrad et al.

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 2009. p. 1641-1648 (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tangermann, MW, Krauledat, M, Grzeska, K, Sagebaum, M, Vidaurre, C, Blankertz, B & Müller, KR 2009, Playing pinball with non-invasive BCI. in Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference, pp. 1641-1648, 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008, Vancouver, BC, Canada, 08/12/8.

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AU - Tangermann, Michael W.

AU - Krauledat, Matthias

AU - Grzeska, Konrad

AU - Sagebaum, Max

AU - Vidaurre, Carmen

AU - Blankertz, Benjamin

AU - Müller, Klaus Robert

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AB - Compared to invasive Brain-Computer Interfaces (BCI), non-invasive BCI systems based on Electroencephalogram (EEG) signals have not been applied successfully for precisely timed control tasks. In the present study, however, we demonstrate and report on the interaction of subjects with a real device: a pinball machine. Results of this study clearly show that fast and well-timed control well beyond chance level is possible, even though the environment is extremely rich and requires precisely timed and 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. The current study shows clearly that very compelling control with excellent timing and dynamics is possible for a non-invasive BCI.

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Playing pinball with non-invasive BCI

Abstract

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Other

ASJC Scopus subject areas

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