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

^*Corresponding author for this work

Technical University of Berlin

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

96 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
Publisher	Neural Information Processing Systems
Pages	1641-1648
Number of pages	8
ISBN (Print)	9781605609492
Publication status	Published - 2009
Externally published	Yes
Event	22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada Duration: 8 Dec 2008 → 11 Dec 2008

Publication series

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

Conference

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

Cite this

Tangermann, M. W., Krauledat, M., Grzeska, K., Sagebaum, M., Vidaurre, C., Blankertz, B., & Müller, K. R. (2009). Playing pinball with non-invasive BCI. In Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference (pp. 1641-1648). (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference). Neural Information Processing Systems.

@inproceedings{1e282a67a5074cc49818e8cd01c31919,

title = "Playing pinball with non-invasive BCI",

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.",

author = "Tangermann, \{Michael W.\} and Matthias Krauledat and Konrad Grzeska and Max Sagebaum and Carmen Vidaurre and Benjamin Blankertz and M{\"u}ller, \{Klaus Robert\}",

year = "2009",

language = "English",

isbn = "9781605609492",

series = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",

publisher = "Neural Information Processing Systems",

pages = "1641--1648",

booktitle = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",

note = "22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 ; Conference date: 08-12-2008 Through 11-12-2008",

}

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, Neural Information Processing Systems, pp. 1641-1648, 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008, Vancouver, BC, Canada, 8/12/08.

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. Neural Information Processing Systems, 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 › peer-review

TY - GEN

T1 - Playing pinball with non-invasive BCI

AU - Tangermann, Michael W.

AU - Krauledat, Matthias

AU - Grzeska, Konrad

AU - Sagebaum, Max

AU - Vidaurre, Carmen

AU - Blankertz, Benjamin

AU - Müller, Klaus Robert

PY - 2009

Y1 - 2009

N2 - 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.

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.

UR - https://www.scopus.com/pages/publications/84858775809

M3 - Conference contribution

AN - SCOPUS:84858775809

SN - 9781605609492

T3 - Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

SP - 1641

EP - 1648

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

PB - Neural Information Processing Systems

T2 - 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008

Y2 - 8 December 2008 through 11 December 2008

ER -

Playing pinball with non-invasive BCI

Abstract

Publication series

Conference

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