A Hybrid Brain-Computer Interface for Smart Home Control

  • Günter Edlinger
  • Clemens Holzner
  • Christoph Guger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6762)


Brain-computer interfaces (BCI) provide a new communication channel between the human brain and a computer without using any muscle activities. Applications of BCI systems comprise communication, restoration of movements or environmental control. Within this study we propose a combined P300 and steady-state visually evoked potential (SSVEP) based BCI system for controlling finally a smart home environment. Firstly a P300 based BCI system was developed and tested in a virtual smart home environment implementation to work with a high accuracy and a high degree of freedom. Secondly, in order to initiate and stop the operation of the P300 BCI a SSVEP based toggle switch was implemented. Results indicate that a P300 based system is very well suitable for applications with several controllable devices and where a discrete control command is desired. A SSVEP based system is more suitable if a continuous control signal is needed and the number of commands is rather limited. The combination of a SSVEP based BCI as a toggle switch to initiate and stop the P300 selection yielded in all subjects very high reliability and accuracy.


Brain-Computer Interface Smart Home P300 SSVEP electroencephalogram 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kubler, A., Perelmouter, J., Taub, E., Flor, H.: A spelling device for the paralysed. Nature 398, 297–298 (1999)CrossRefGoogle Scholar
  2. 2.
    Sellers, E.W., Krusienski, D.J., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. Biol. Psychol. 73, 242–252 (2006)CrossRefGoogle Scholar
  3. 3.
    Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., Edlinger, G.: How many people are able to control a P300-based brain-computer interface (BCI)? Neuroscience letters 462, 94–98 (2009)CrossRefGoogle Scholar
  4. 4.
    Allison, B., Luth, T., Valbuena, D., Teymourian, A., Volosyak, I., Graser, A.: BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? IEEE Transactions on Neural Systems and Rehabilitation Engineering 18(2), 107–116 (2010)CrossRefGoogle Scholar
  5. 5.
    Friman, O., Volosyak, I., Graser, A.: Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Trans. Biomed. Eng. 54, 742–750 (2007)CrossRefGoogle Scholar
  6. 6.
    Pfurtscheller, G., Neuper, C., Muller, G.R., Obermaier, B., Krausz, G., Schlogl, A., Scherer, R., Graimann, B., Keinrath, C., Skliris, D., Wortz, M., Supp, G., Schrank, C.: Graz-BCI: state of the art and clinical applications. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 177–180 (2003)CrossRefGoogle Scholar
  7. 7.
    Pfurtscheller, G., Allison, B.Z., Brunner, C., Bauernfeind, G., Solis-Escalante, T., Scherer, R., Zander, T.O., Mueller-Putz, G., Neuper, C., Birbaumer, N.: The Hybrid BCI. Front Neurosci. 4, 42 (2010)Google Scholar
  8. 8.
    Hong, B., Guo, F., Liu, T., Gao, X., Gao, S.: N200-speller using motion-onset visual response. Clin. Neurophysiol. 120, 1658–1666 (2009)CrossRefGoogle Scholar
  9. 9.
    Komatsu, T., Hata, N., Nakajima, Y., Kansaku, K.: A non-training EEG-based BMI system for environmental control. Neurosci. Res. 61(suppl.1), S251 (2008)Google Scholar
  10. 10.
    Edlinger, G., Holzner, C., Groenegress, C., Guger, C., Slater, M.: Goal-Oriented Control with Brain-Computer Interface. In: HCI 2009, vol. 16, pp. 732–740 (2009)Google Scholar
  11. 11.
    Haihong, Z., Cuntai, G., Chuanchu, W.: Asynchronous P300-Based Brain–Computer Interfaces: A Computational Approach With Statistical Models. IEEE Transactions on Biomedical Engineering 55(6), 1754–1763 (2008)CrossRefGoogle Scholar
  12. 12.
    Nijboer, F., Sellers, E.W., Mellinger, J., Jordan, M.A., Matuz, T., Furdea, A., Halder, S., Mochty, U., Krusienski, D.J., Vaughan, T.M., Wolpaw, J.R., Birbaumer, N., Kubler, A.: A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin. Neurophysiol. 119, 1909–1916 (2008)CrossRefGoogle Scholar
  13. 13.
    Allison, B.Z., McFarland, D., Schalk, G., Zheng, S.D., et al.: Towards an independent brain-computer interface using steady state visual evoked potentials. In: Brain-computer interface systems: progress and prospects BCI Meeting 2005–workshop on signals and recording methods, pp. 1388–2457 (2005)Google Scholar
  14. 14.
    Dan, Z., Xiaorong, G., Shangkai, G., Engel, A.K., Maye, A.: An independent brain-computer interface based on covert shifts of non-spatial visual attention 539–542 (September 3, 2009)Google Scholar
  15. 15.
    Friman, O., Volosyak, I., Graser, A.: Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces. IEEE Transactions on Biomedical Engineering 54(4), 742–750 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Günter Edlinger
    • 1
  • Clemens Holzner
    • 1
  • Christoph Guger
    • 1
  1. 1.Guger Technologies OG and g.tec medical engineering GmbHGrazAustria

Personalised recommendations