Unsupervised Online Calibration of a c-VEP Brain-Computer Interface (BCI)

  • Martin Spüler
  • Wolfgang Rosenstiel
  • Martin Bogdan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8131)


Brain-Computer Interfaces (BCIs) can be used to give paralyzed patients a means for communication. But so far, only supervised methods have been used for calibration of an online BCI. In this paper we present a method that allows to calibrate a BCI online and unsupervised. Based on offline data we show that the unsupervised calibration method works and validate the results in an online experiment with 8 subjects, who were able to control the BCI with an average accuracy of 85 %. We thereby have shown for the first time that an online unsupervised calibration of a BCI is possible and allows for successful BCI control.


Brain-Computer interface (BCI) unsupervised learning 


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  1. 1.
    Kübler, A., Birbaumer, N.: Brain-computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? Clinical Neurophysiology 119(11), 2658–2666 (2008)CrossRefGoogle Scholar
  2. 2.
    Spüler, M., Rosenstiel, W., Bogdan, M.: Online adaptation of a c-VEP Brain-Computer Interface (BCI) based on Error-related potentials and unsupervised learning. Plos One 7(12), e51077 (2012), doi:10.1371/journal.pone.0051077Google Scholar
  3. 3.
    Spüler, M., Rosenstiel, W., Bogdan, M.: Adaptive SVM-based classification increases performance of a MEG-based Brain-Computer Interface (BCI). In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012, Part I. LNCS, vol. 7552, pp. 669–676. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Eren, S., Grosse-Wentrup, M., Buss, M.: Unsupervised classification for non-invasive brain-computer-interfaces. In: Proc. Automed Workshop, Düsseldorf, Germany, pp. 65–66 (2007)Google Scholar
  5. 5.
    Spüler, M., Rosenstiel, W., Bogdan, M.: One Class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI). In: Proceedings of 20th European Symposium on Artificial Neural Networks (ESANN 2012), Bruges, Belgium, pp. 103–108 (April 2012)Google Scholar
  6. 6.
    Schölkopf, B., Platt, C.: Estimating the support of a High-Dimensional Distribution. Neural Computation (2001)Google Scholar
  7. 7.
    Hartigan, J.A.: Clustering Algorithms, 99th edn. John Wiley & Sons, Inc., New York (1975)zbMATHGoogle Scholar
  8. 8.
    Spüler, M., Rosenstiel, W., Bogdan, M.: Unsupervised BCI calibration as possibility for communication in CLIS patients? In: Proceedings of the Fifth International Brain-Computer Interface Meeting (2013), doi:10.3217/978-3-85125-260-6-122Google Scholar
  9. 9.
    Kelly, S.P., Lalor, E.C., Reilly, R.B., Foxe, J.J.: Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. IEEE TNSRE 13(2), 172–177 (2005)Google Scholar
  10. 10.
    Zhang, D., Maye, A., Gao, X., Hong, B., Engel, A.K., Gao, S.: An independent brain-computer interface using covert non-spatial visual selective attention. Journal of Neural Engineering 7(1), 016010 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Martin Spüler
    • 1
  • Wolfgang Rosenstiel
    • 1
  • Martin Bogdan
    • 1
    • 2
  1. 1.Wilhelm-Schickard-Institute for Computer ScienceUniversity of TübingenGermany
  2. 2.Computer EngineeringUniversity of LeipzigGermany

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