Subject-Specific Methodology in the Frequency Scanning Phase of SSVEP-Based BCI

  • Izabela RejerEmail author
  • Łukasz Cieszyński
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 534)


Steady State Visual Evoked Potentials (SSVEPs) often used in Brain Computer Interfaces (BCIs) differ across subjects. That is why a new SSVEP-based BCI user should always start the session from the frequency scanning phase. During this phase the stimulation frequencies evoking the most prominent SSVEPs are determined. In our opinion not only the stimulation frequencies specific for the given user should be chosen in the scanning phase but also the methodology used for SSVEP detection. The paper reports the results of a survey whose aim was to find out whether using subject specific methodology for identifying stimulation frequencies would increase the number of frequencies found. We analyzed three factors: length of time window used for power spectrum calculation, combination of channels, and number of harmonics used for SSVEP detection. According to the outcome of the experiment (performed with 6 subjects) the mean drop in the number of SSVEPs detected with any other but the best combination of factors was very large for all subjects (from 31.52 % for subject S3 to 51.76 % for subject S4).


SSVEP BCI Brain Computer Interface EEG 


  1. 1.
    Fernandez-Vargas, J., Pfaff, H.U., Rodriguez, F.B., Varona, P.: Assisted closed-loop optimization of SSVEP-BCI efficiency. Front. Neural Circ. 7, 1–5 (2013)Google Scholar
  2. 2.
    Allison, B.Z., McFarland, D.J., Schalk, G., Zheng, S.D., Jackson, M.M., Wolpaw, J.R.: Towards an independent brain–computer interface using steady state visual evoked potentials. Clin. Neurophysiol. 119(2), 399–408 (2008)CrossRefGoogle Scholar
  3. 3.
    Vialatte, F.B., Maurice, M., Dauwels, M., Cichocki, A.: Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog. Neurobiol. 90(4), 418–438 (2010)CrossRefGoogle Scholar
  4. 4.
    Cuffin, B.N.: Effects of local variations in skull and scalp thickness on EEG’s and MEG’s. IEEE Trans. Biomed. Eng. 40(1), 42–48 (1993)CrossRefGoogle Scholar
  5. 5.
    Luo, A., Sullivan, T.J.: A user-friendly SSVEP-based brain–computer interface using a time-domain classifier. J. Neural Eng. 7(2), 1–10 (2010)CrossRefGoogle Scholar
  6. 6.
    Wu, Z., Su, S.: A dynamic selection method for reference electrode in SSVEP-based BCI. PLoS ONE 9(8), e104248 (2014)CrossRefGoogle Scholar
  7. 7.
    Jasper, H.H.: The ten-twenty electrode system of the international federation in electroencephalography and clinical neurophysiology. EEG J. 10, 371–375 (1958)Google Scholar
  8. 8.
    Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., Lécuyer, A.: OpenViBE: an open-source software platform to design, test and use brain-computer interfaces in real and virtual environments. Presence: Teleoperators Virtual Environ. 19(1), 35–53 (2010)CrossRefGoogle Scholar
  9. 9.
    Paulus, W.: Elektroretinographie (ERG) und visuell evozierte Potenziale (VEP). In: Buchner, H., Noth, J. (eds.) Evozierte Potenziale, neurovegetative Diagnostik, Okulographie: Methodik und klinische Anwendungen, pp. 57–65. Thieme, Stuttgart (2005)Google Scholar
  10. 10.
    Regan, D.: Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Elsevier, New York (1989)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology in SzczecinSzczecinPoland

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