Using Coherence for Robust Online Brain-Computer Interface (BCI) Control

  • Martin Spüler
  • Wolfgang Rosenstiel
  • Martin Bogdan
Part of the Communications in Computer and Information Science book series (CCIS, volume 438)


A Brain-Computer Interface (BCI) enables the user to control a computer by brain activity only. In this paper we investigated the use of different brain connectivity methods to control a Magnetoencephalography (MEG)-based Brain-Computer Interface (BCI). We compared the use of coherence, phase synchronisation and a widely used method for spectral power estimation and found coherence to be a more robust feature extraction method, when using the BCI over a longer time interval across sessions. To validate these results we implemented an online BCI system using coherence and could show that coherence also performed more robust in an online setting than traditional methods.


Brain connectivity Brain-Computer Interface (BCI) Coherence Magnetoencephalogrpahy (MEG) non-stationarity 


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  1. 1.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clinical Neurophysiology 113(6), 767–791 (2002)CrossRefGoogle Scholar
  2. 2.
    Mellinger, J., Schalk, G., Braun, C., Preissl, H., Rosenstiel, W., Birbaumer, N., Kübler, A.: An MEG-based brain-computer interface (BCI). NeuroImage 36(3), 581–593 (2007)CrossRefGoogle Scholar
  3. 3.
    Kaiser, J., Walker, F., Leiberg, S., Lutzenberger, W.: Cortical oscillatory activity during spatial echoic memory. European Journal of Neuroscience 21(2), 587–590 (2005)CrossRefGoogle Scholar
  4. 4.
    Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proceedings of the IEEE 89(7), 1123–1134 (2001)CrossRefGoogle Scholar
  5. 5.
    Spiegler, A., Graimann, B., Pfurtscheller, G.: Phase coupling between different motor areas during tongue-movement imagery. Neuroscience Letters 369(1), 50–54 (2004)CrossRefGoogle Scholar
  6. 6.
    Gysels, E., Celka, P.: Phase synchronization for the recognition of mental tasks in a brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 12(4), 406–415 (2004)CrossRefGoogle Scholar
  7. 7.
    Brunner, C., Scherer, R., Graimann, B., Supp, G., Pfurtscheller, G.: Online Control of a Brain-Computer Interface Using Phase Synchronization. IEEE Transactions on Biomedical Engineering 53(12), 2501–2506 (2006)CrossRefGoogle Scholar
  8. 8.
    Wei, Q., Wang, Y., Gao, X., Gao, S.: Amplitude and phase coupling measures for feature extraction in an EEG-based brain-computer interface. Journal of Neural Engineering 4(2), 120 (2007)CrossRefGoogle Scholar
  9. 9.
    Hamner, B., Leeb, R., Tavella, M., del Millan, J.R.: Phase-based features for motor imagery brain-computer interfaces. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 2578–2581 (2011)Google Scholar
  10. 10.
    Bensch, M., Bogdan, M., Rosenstiel, W.: Phase Synchronization in MEG for Brain-Computer Interfaces. In: Proceedings of the 3rd Int. Brain-Computer Interface Workshop, Graz, pp. 18–19 (September 2006)Google Scholar
  11. 11.
    Krusienski, D.J., Grosse-Wentrup, M., Galn, F., Coyle, D., Miller, K.J., Forney, E., Anderson, C.W.: Critical issues in state-of-the-art brain-computer interface signal processing. Journal of Neural Engineering 8(2), 025002 (2011)Google Scholar
  12. 12.
    Spüler, M., Rosenstiel, W., Bogdan, M.: Principal component based covariate shift adaption to reduce non-stationarity in a meg-based brain-computer interface. EURASIP Journal on Advances in Signal Processing 2012(1), 129 (2012)CrossRefGoogle Scholar
  13. 13.
    Priestley, M.B.: Spectral analysis and time series. Academic Press, London (1981)zbMATHGoogle Scholar
  14. 14.
    Bensch, M., Mellinger, J., Bogdan, M., Rosenstiel, W.: A multiclass BCI using MEG. In: Proceedings of the 4th Int. Brain-Computer Interface Workshop, Graz, pp. 191–196 (September 2008)Google Scholar
  15. 15.
    Spüler, M., Rosenstiel, W., Bogdan, M.: A fast feature selection method for high-dimensional MEG BCI data. In: Proceedings of the 5th Int. Brain-Computer Interface Conference, Graz, pp. 24–27 (September 2011)Google Scholar
  16. 16.
    Vapnik, V.N.: Statistical Learning Theory, 1st edn. Wiley-Interscience (September 1998)Google Scholar
  17. 17.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines (2001), Software available at
  18. 18.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B., et al.: A review of classification algorithms for eeg-based brain-computer interfaces. Journal of Neural Engineering, 4 (2007)Google Scholar
  19. 19.
    Sugiyama, M., Krauledat, M., Müller, K.-R.: Covariate shift adaptation by importance weighted cross validation. J. Mach. Learn. Res. 8, 985–1005 (2007)zbMATHGoogle Scholar
  20. 20.
    Vidaurre, C., Blankertz, B.: Towards a cure for bci illiteracy. Brain Topography 23, 194–198 (2010), doi:10.1007/s10548-009-0121-6CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

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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