A Hybrid BCI for Gaming

  • Xing Song
  • Shane XieEmail author
  • Wei Meng


As interfaces between the brain and computers, EEG-based BCIs are useful to the control of assistive devices and technologies. This chapter proposes a hybrid EEG-based BCI for controlling a video game using EEG rhythms and SSVEPs. It builds on the work presented in prior chapters, applying it to gaming that involves training aspects and more complex commands. An EEG cap with seven active electrodes was used to collect users’ brain signals. The signals were passed through noise suppression and classification using the Fast Fourier Transform (FFT), the ANBF and adaptive thresholds.


  1. 1.
    Mouchere, H., E. Anquetil, and N. Ragot. On-line writer adaptation for handwriting recognition using fuzzy inference systems. in Eighth International Conference on Document Analysis and Recognition, 2005.Google Scholar
  2. 2.
    Lalor, E., et al., Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. EURASIP Journal on Advances in Signal Processing, 2005. 2005(19): p. 706906.Google Scholar
  3. 3.
    Reuderink, B., Games and brain-computer interfaces: The state of the art, University of Twente, The Netherlands, 2008.Google Scholar
  4. 4.
    Pineda, J.A., et al., Learning to control brain rhythms: Making a brain-computer interface possible. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003. 11(2): p. 181–184.Google Scholar
  5. 5.
    Bos, D.P.O., et al. Human-computer interaction for BCI games: Usability and user experience. in International Conference on Cyberworlds, 2010.Google Scholar
  6. 6.
    Andersen, R.A., S. Musallam, and B. Pesaran, Selecting the signals for a brain-machine interface. Current Opinion in Neurobiology, 2004. 14(6): p. 720–726.Google Scholar
  7. 7.
    Leuthardt, E.C., et al., A brain-computer interface using electrocorticographic signals in humans. Journal of Neural Engineering, 2004. 1(2): p. 63–71.Google Scholar
  8. 8.
    Margalit, E., et al., Visual and electrical evoked response recorded from subdural electrodes implanted above the visual cortex in normal dogs under two methods of anesthesia. Journal of Neuroscience Methods, 2003. 123(2): p. 129–137.Google Scholar
  9. 9.
    Wallois, F., et al., EEG-NIRS in epilepsy in children and neonates. Neurophysiologie Clinique/Clinical Neurophysiology, 2010. 40(5-6): p. 281-292.Google Scholar
  10. 10.
    Gerven, M.V., et al., The brain-computer interface cycle. Journal of Neural Engineering, 2009. 6(4): p. 041001–041010.Google Scholar
  11. 11.
    Siniatchkin, M., P. Kropp, and W.-D. Gerber, Neurofeedback—The significance of reinforcement and the search for an appropriate strategy for the success of self-regulation. Applied Psychophysiology and Biofeedback, 2000. 25(3): p. 167–175.Google Scholar
  12. 12.
    Wolpaw, J.R., et al., Brain-computer interfaces for communication and control. Clinical Neurophysiology, 2002. 113(6): p. 767–791.Google Scholar
  13. 13.
    Sutter, E.E., The brain response interface: Communication through visually-induced electrical brain responses. Journal of Microcomputer Applications, 1992. 15(1): p. 31–45.Google Scholar
  14. 14.
    P.M. Dibartolo, T.A. Brown, and D.H. Barlow, Effects of anxiety on attentional allocation and task performance: An information processing analysis. Behaviour Research and Therapy, 1997. 35: p. 1101–1111.Google Scholar
  15. 15.
    Soyuer, O.U., et al., Classification and follow-up of pediatric patients with absence epilepsy. Epilepsia, 2006. 47: p. 152–152.Google Scholar
  16. 16.
    Bussink, D., Towards the first HMI BCI game, University of Twente, The Netherlands, 2008.Google Scholar
  17. 17.
    Allanson, J. and J. Mariani. Mind over virtual matter: Using virtual environments for neurofeedback training. in Virtual Reality, 1999.Google Scholar
  18. 18.
    Bayliss, J.D., Use of the evoked potential P3 component for control in a virtual apartment. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003. 11(2): p. 113–116.Google Scholar
  19. 19.
    Liu, M., et al. Development of EEG biofeedback system based on virtual reality environment. in 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005.Google Scholar
  20. 20.
    Caracillo, R.C. and M.C.F. Castro. Classification of executed upper limb movements by means of EEG. Biosignals and Biorobotics Conference, February 18–20, 2013.Google Scholar
  21. 21.
    Khushaba, R.N., et al., Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems with Applications, 2013. 40(9): p. 3803–3812.Google Scholar
  22. 22.
    Lalor E., et al. Brain computer interface based on the steady-state VEP for immersive gaming control. Biomed. Tech. 2004, 49(1): p. 63–4.Google Scholar
  23. 23.
    Ou, B. and et al., A high performance sensorimotor beta rhythm-based brain–computer interface associated with human natural motor behavior. Journal of Neural Engineering, 2008. 5(1): p. 24.Google Scholar
  24. 24.
    Kayagil, T., et al. Binary EEG control for two-dimensional cursor movement: An online approach. in IEEE/ICME International Conference on Complex Medical Engineering, 2007.Google Scholar
  25. 25.
    Lehtonen, J., et al., Online classification of single EEG trials during finger movements. IEEE Transactions on Biomedical Engineering, 2008. 55(2): p. 713–720.Google Scholar
  26. 26.
    Nuttall, A., Some windows with very good sidelobe behavior. IEEE Transactions on Acoustics, Speech and Signal Processing, 1981. 29(1): p. 84–91.Google Scholar
  27. 27.
    McDaid, A.J., S. Xing, and S.Q. Xie. Brain controlled robotic exoskeleton for neurorehabilitation. in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2013. Wollongong, Australia.Google Scholar
  28. 28.
    Müller, K.R., C.W. Anderson, and G.E. Birch, Linear and nonlinear methods for brain-computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003. 11(2): p. 165–169.Google Scholar
  29. 29.
    Garrett, D., et al., Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003. 11(2): p. 141–144.Google Scholar
  30. 30.
    Leuthardt, E.C., et al., A brain-computer interface using electrocorticographic signals in humans. Journal of Neural Engineering, 2004. 1(2): p. 63–71.Google Scholar
  31. 31.
    Chao, Z.C., Y. Nagasaka, and N. Fujii, Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkey. Frontiers in Neuroengineering, 2010. 3.Google Scholar
  32. 32.
    Oldfield, R.C., Assessment and analysis of handedness - Edinburgh Inventory. Neuropsychologia, 1971. 9(1): p. 97–113.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringUniversity of LeedsLeedsUnited Kingdom
  2. 2.Department of Mechanical EngineeringThe University of AucklandAucklandNew Zealand
  3. 3.School of Information EngineeringWuhan University of TechnologyWuhanChina
  4. 4.The University of AucklandAucklandNew Zealand

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