Toward Multi-brain Communication: Collaborative Spelling with a P300 BCI

  • Christoph Kapeller
  • Rupert Ortner
  • Gunther Krausz
  • Markus Bruckner
  • Brendan Z. Allison
  • Christoph Guger
  • Günter Edlinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8534)


In a brain-computer interface (BCI), users perform specific mental tasks to convey messages or commands through direct measures of brain activity. Typically, users must perform each mental task for two or more seconds before their brain activity is distinct enough for accurate classification. In P300 BCIs, this usually entails silently counting specific flashes three or more times. Although numerous articles have explored the prospect of a P300 BCI that relies on only one flash, results consistently show that the resulting accuracy would be too low for effective communication. The goal of this article was to introduce a new way to reduce the time to identify a message or command. Instead of relying on brain activity from one subject, our system utilized brain activity from eight subjects performing a single trial. Hence, the system could rely on an average based on eight trials, which is more than sufficient for adequate classification, even though each subject contributed only one trial. Results confirmed that all eight subjects could not have attained effective control with a single trial, but could attain 100% accuracy when the other seven subjects’ data were also used. This is the first time that people worked together to accomplish a goal with a BCI, and could encourage future research into collaborative brain-based communication and control.


brain-computer interface (BCI) brain-machine interface (BMI) multi-brain computing multi-brain gaming EEG ERP P300 spelling 


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  1. 1.
    Allison, B.Z., Leeb, R., Brunner, C., Müller-Putz, G.R., Bauernfeind, G., Kelly, J.W., Neuper, C.: Toward smarter BCIs: extending BCIs through hybridization and intelligent control. Journal of Neural Engineering 9(1) (2012)Google Scholar
  2. 2.
    Bayliss, J.D.: Use of the evoked potential P3 component for control in a virtual apartment. IEEE Trans. Neural. 11(2), 113–116 (2003)MathSciNetGoogle Scholar
  3. 3.
    Billinger, M., Daly, I., Kaiser, V., Jin, J., Allison, B.Z., Müller-Putz, G.R., Brunner, C.: Is it significant? Guidelines for reporting BCI performance. In: Allison, B.Z., Dunne, S., Leeb, R., Millan, J., Nijholt, A. (eds.) Towards Practical BCIs: Bridging the Gap from Research to Real-World Applications, pp. 333–354. Springer, Heidelberg (2013)Google Scholar
  4. 4.
    Brunner, P., Ritaccio, A.L., Emrich, J.F., Bischof, H., Schalk, G.: Rapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG). Frontiers in Neuroscience 5 (2011)Google Scholar
  5. 5.
    Farwell, L.A., Donchin, E.: Talking off the top of your head. Electroenceph. Clin. Neurophysiol. 70, 510–523 (1988)CrossRefGoogle Scholar
  6. 6.
    Fazel-Rezai, R., Allison, B.Z., Sellers, E., Guger, C., Kleih, S., Kübler, A.: P300 brain computer interfaces: Current challenges and future directions. Frontiers in Neuroengineering 5, 14 (2012)CrossRefGoogle Scholar
  7. 7.
    Frye, G.E., Hauser, C.K., Townsend, G., Sellers, E.W.: Suppressing flashes of items surrounding targets during calibration of a P300-based brain-computer interface improves performance. Journal of Neural Engineering 8(2), Epub. (2011)Google Scholar
  8. 8.
    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(1), 94–98 (2009)CrossRefGoogle Scholar
  9. 9.
    Guger, C., Krausz, G., Allison, B.Z., Edlinger, G.: A comparison of dry and gel-based electrodes for P300 BCIs. Frontiers in Neuroscience 6, 60 (2012)Google Scholar
  10. 10.
    Serby, H., Yom-Tov, E., Inbar, G.: An improved P300-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 13(1), 89–98 (2005)CrossRefGoogle Scholar
  11. 11.
    Jin, J., Allison, B.Z., Wang, X., Neuper, C.: A combined brain-computer interface based on P300 potentials and motion-onset visual evoked potentials. Journal of Neuroscience Methods (2012)Google Scholar
  12. 12.
    Kaufmann, T., Schulz, S.M., Grünzinger, C., Kübler, A.: Flashing characters with famous faces improves ERP-based brain-computer interface performance. Journal of Neural Engineering 8(5), Epub. (2011)Google Scholar
  13. 13.
    Lalor, E.C., Kelly, S.P., Finucane, C., Burke, R., Smith, R., Reilly, R.B., McDarby, G.: File2005. Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. Eurasip Journal on Applied Signal Processing, 3156–3164 (2005)Google Scholar
  14. 14.
    McFarland, D.J., Sarnacki, W.A., Wolpaw, J.R.: Electroencephalographic (EEG) control of three-dimensional movement. Journal of Neural Engineering 7(3), Epub. (2010)Google Scholar
  15. 15.
    Meinicke, P., Kaper, M., Hoppe, F., Huemann, M., Ritter, H.: Improving transfer rates in brain computer interface: a case study. Neural Inf. Proc. Syst. 2002, 1107–1114 (2002)Google Scholar
  16. 16.
    Mühl, C., Nijholt, A., Allison, B.Z., Dunne, S., Heylen, D.: Affective Brain-Computer Interfaces (aBCI 2011). Affective Communication and Intelligent Interaction (2), 435 (2011)Google Scholar
  17. 17.
    Scherer, R., Faller, J., Balderas, D., Friedrich, E.V.C., Pröll, M., Allison, B.Z., Müller-Putz, G.: Brain-computer interfacing: More than the sum of its parts. Journal of Soft Computing (2012), doi:10.1007/s00500-012-0895-4Google Scholar
  18. 18.
    Sellers, E.W., Donchin, E.: A P300-based brain-computer interface: initial tests by ALS patients. Clin. Neurophysiol. 117(3), 538–548, Epub. (2006)Google Scholar
  19. 19.
    Townsend, G., LaPallo, B.K., Boulay, C.B., Krusienski, D.J., Frye, G.E., Hauser, C.K., Schwartz, N.E., Vaughan, T.M., Wolpaw, J.R., Sellers, E.W.: A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns. Clinical Neurophysiology 121(7), 1109–1120 (2010)CrossRefGoogle Scholar
  20. 20.
    Vidaurre, C., Sannelli, C., Müller, K.R., Blankertz, B.: Co-adaptive calibration to improve BCI efficiency. Journal of Neural Engineering 8(2), Epub. (2011)Google Scholar
  21. 21.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clinical Neurophysiology 113, 767–791 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Christoph Kapeller
    • 1
  • Rupert Ortner
    • 1
  • Gunther Krausz
    • 1
  • Markus Bruckner
    • 1
  • Brendan Z. Allison
    • 2
  • Christoph Guger
    • 1
  • Günter Edlinger
    • 1
  1. 1.Guger Technologies OGGrazAustria
  2. 2.Department of Cognitive ScienceUniversity of California at San DiegoLa JollaUSA

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