Optimization of External Stimulus Features for Hybrid Visual Brain–Computer Interface

  • Deepak KapgateEmail author
  • Dhananjay Kalbande
  • Urmila Shrawankar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)


To make brain–computer interface (BCI) systems effective, complex cortical processing algorithms get attention in past few years rather than optimizing different external stimulus properties. Work on finding optimal stimulus properties is still incomplete. The objective of this study is to find stimulus properties that evoke stronger cortical responses. Stimulus parameters like different size, color and frequency are analyzed for hybrid visual BCI based on steady-state visual-evoked potentials (SSVEP) and event-related potentials (P300) (Hybrid SSVEP + P300 BCI). Study revealed that stimulus with frequency—15 Hz, color—red and size (in angular degree) \(7.96^{\circ }\) evoked stronger cortical potentials and higher signal to noise ratio (SNR). Further, green and yellow colors are found comfortable as compared to red which generates more fatigue. Maximum cortical mean SSVEP amplitude calculated is 5.1 \(\upmu \)V and for P300 is 4.9 \(\upmu \)V.


Hybrid brain–computer interface Stimulus features Visual-evoked potential’s amplitude Optimization 



Authors thankful to Director G. H. Raisoni College of Engineering, Nagpur University for valuable support.


  1. 1.
    L. Fernando et al., Brain computer interface, a review. Sensors 12, 1211–1279 (2012). Scholar
  2. 2.
    E. Yin et al., A speedy hybrid BCI spelling approach combining P300 and SSVEP. IEEE Trans. Biomed. Eng. 61(2), 473–483 (2014)CrossRefGoogle Scholar
  3. 3.
    R.C. Panicker et al., An asynchronous P300 BCI with SSVEP based control state detection. IEEE Trans. Biomed. Eng. 58(6), 1781–1788 (2011)CrossRefGoogle Scholar
  4. 4.
    L. Bi. et al., A speed and direction based cursor control system with P300 and SSVEP. Elsevier J. Biomed. Signal Process. Control 14(126–133) (2014)CrossRefGoogle Scholar
  5. 5.
    H. Cecotti, Reliable Visual Stimuli on LCD screens for SSVEP based BCI. 18th European Signal Processing Conference (2010)Google Scholar
  6. 6.
    L. Chu et al., Influence of stimulus color on steady state visual evoked potentials. Spr. J. Adv. Intell. Syst. Comput. 531, 499–508 (2016). Scholar
  7. 7.
    A. Duszyk et al., Towards an optimization of stimulus parameters for brain-computer interfaces based on steady state visual evoked potentials. PLoS ONE 9(11) (2014). Scholar
  8. 8.
    R. Mehta, R.J. Zhu, Blue or red? Exploring the effect of color on cognitive task performances. Science 323(5918), 1226–9 (2009). PMid:19197022CrossRefGoogle Scholar
  9. 9.
    M. Jukiewicz et al., Stimuli design for SSVEP-based brain computer-interface. Int. J. Electron. Telecommun. 62(2), 109–113 (2016)CrossRefGoogle Scholar
  10. 10.
    A.C. Moller et al., Basic hue-meaning associations. Emotion 9(6), 898–902 (2009). PMid:20001133CrossRefGoogle Scholar
  11. 11.
    R.K. Grigoryan et al., Visual Stimuli for P300-Based Brain-Computer Interfaces: Color, Shape, and Mobility, vol. 73, no. 2 (Vestnik Moskovskogo Universiteta, Seriya 16: Biologiya, 2018), pp. 111–117Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Deepak Kapgate
    • 1
    Email author
  • Dhananjay Kalbande
    • 1
  • Urmila Shrawankar
    • 1
  1. 1.Department of Information TechnologyG. H. Raisoni College of EngineeringNagpurIndia

Personalised recommendations