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Non-invasive Brain-Computer Interfaces: Enhanced Gaming and Robotic Control

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6691)

Abstract

The performance of non-invasive electroencephalogram-based (EEG) brain-computer interfacing (BCI) has improved significantly in recent years. However, remaining challenges include the non-stationarity and the low signal-to-noise ratio (SNR) of the EEG, which limit the bandwidth and hence the available applications. In this paper, we review ongoing research in our labs and introduce novel concepts and applications. First, we present an enhancement of the 3-class self-paced Graz-BCI that allows interaction with the massive multiplayer online role playing game World of Warcraft. Second, we report on the long-term stability and robustness of detection of oscillatory components modulated by distinct mental tasks. Third, we describe a scalable, adaptive learning framework, which allows users to teach the BCI new skills on-the-fly. Using this hierarchical BCI, we successfully train and control a humanoid robot in a virtual home environment.

Keywords

  • Linear Discriminant Analysis
  • Motor Imagery
  • Humanoid Robot
  • Common Spatial Pattern
  • Radial Basis Function Model

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-642-21501-8_45
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References

  1. Mason, S.G., Bashashati, A., Fatourechi, M., Navarro, K.F., Birch, G.E.: A com- prehensive survey of brain interface technology designs. Ann. Biomed. Eng. 35(2), 137–169 (2007)

    CrossRef  Google Scholar 

  2. Scherer, R., Schlögl, A., Lee, F., Bischof, H., Jans̃a, J., Pfurtscheller, G.: The Self-Paced Graz Brain-Computer Interface: Methods and Applications. Computational Intelligence and Neuroscience, Article ID 79826 (2007)

    Google Scholar 

  3. Friedrich, E., Scherer., R., Neuper, C.: Consistency over time and across tasks of brain-computer interface-relevant mental strategies. In: Proc. TOBI Workshop, Graz, Austria, February 3-4, p. 68 (2010)

    Google Scholar 

  4. Ramoser, H., Müller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Biomed. Eng. 8(4), 441–446 (2000)

    Google Scholar 

  5. Bell, C.J., Shenoy, P., Chalodhorn, R., Rao, R.P.N.: Control of a humanoid robot by a noninvasive brain–computer interface in humans. J. Neural. Eng. 5, 214–220 (2008)

    CrossRef  Google Scholar 

  6. Chung, M., Cheung, W., Scherer, R., Rao, R.P.N.: Towards Hierarchical BCIs for Robotic Control. In: Proc. IEEE EMBS NE 2011, Cancun, Mexico (2011) (to appear)

    Google Scholar 

  7. Rasmussen, C.E.: Gaussian Processes in Machine Learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) Machine Learning 2003. LNCS (LNAI), vol. 3176, pp. 63–71. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Scherer, R. et al. (2011). Non-invasive Brain-Computer Interfaces: Enhanced Gaming and Robotic Control. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21501-8_45

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  • DOI: https://doi.org/10.1007/978-3-642-21501-8_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21500-1

  • Online ISBN: 978-3-642-21501-8

  • eBook Packages: Computer ScienceComputer Science (R0)