Perception and Manipulation of Game Control

  • Danny Plass-Oude Bos
  • Bram van de Laar
  • Boris Reuderink
  • Mannes Poel
  • Anton Nijholt
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 136)


Brain-computer interfaces do not provide perfect recognition of user input, for similar reasons as natural input modalities. How well can users assess the amount of control they have, and how much control do they need? We describe an experiment where we manipulated the control users had in a keyboard-controlled browser game. The data of 211 runs from 87 individuals indicates a significant linear correlation between users’ sense of control and the amount of control they really had in terms of mutual information (not accuracy!). If users know what they put in, they can assess quite well how much control they have over the system. In our case, from an amount of control of above 0.68 bits in mutual information (a 5-class accuracy of 65%), this aspect of control no longer seems to be the critical factor for finishing the game. Deliberate manipulation of perception may offer a way to make imperfect, uncertain input modalities more acceptable, especially in combination with games.


Human-computer interaction brain-computer interfaces manipulation of control perception of control 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Deng, L., Huang, X.: Challenges in adopting speech recognition. Communications of the ACM 47(1), 69–75 (2004)CrossRefGoogle Scholar
  2. 2.
    Jacob, R., Karn, K.: Eye tracking in human-computer interaction and usability research: Ready to deliver the promises. Mind 2(3), 4 (2003)Google Scholar
  3. 3.
    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
  4. 4.
    Plass-Oude Bos, D., Gürkök, H., Reuderink, B., Poel, M.: Improving BCI performance after classification. In: Proceedings of the 14th ACM International Conference on Multimodal Interaction, pp. 587–594. ACM (2012)Google Scholar
  5. 5.
    van de Laar, B., Plass-Oude Bos, D., Reuderink, B., Poel, M., Nijholt, A.: How much control is enough? Influence of unreliable input on user experience. IEEE Transactions on Cybernetics 43(6), 1584–1592 (2013)CrossRefGoogle Scholar
  6. 6.
    Plass-Oude Bos, D., Poel, M., Nijholt, A.: A study in user-centered design and evaluation of mental tasks for BCI. In: Lee, K.-T., Tsai, W.-H., Liao, H.-Y.M., Chen, T., Hsieh, J.-W., Tseng, C.-C. (eds.) MMM 2011 Part II. LNCS, vol. 6524, pp. 122–134. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Nijholt, A., Tan, D.: Playing with your brain: Brain-computer interfaces and games. In: Proceedings of the International Conference on Advances in Computer Entertainment Technology, pp. 305–306. ACM (2007)Google Scholar
  8. 8.
    Nijholt, A., Plass-Oude Bos, D., Reuderink, B.: Turning shortcomings into challenges: Brain–computer interfaces for games. Entertainment Computing 1(2), 85–94 (2009)CrossRefGoogle Scholar
  9. 9.
    Graimann, B., Allison, B., Gräser, A.: New applications for non-invasive brain-computer interfaces and the need for engaging training environments. In: BRAINPLAY 2007 Brain-Computer Interfaces and Games Workshop at ACE (Advances in Computer Entertainment), pp. 25–28 (2007)Google Scholar
  10. 10.
    Allan, L.G., Jenkins, H.M.: The judgment of contingency and the nature of the response alternatives. Canadian Journal of Psychology 34(1), 1 (1980)CrossRefGoogle Scholar
  11. 11.
    Thompson, S., Armstrong, W., Thomas, C.: Illusions of control, underestimations, and accuracy: A control heuristic explanation. Psychological Bulletin 123(2), 143 (1998)CrossRefGoogle Scholar
  12. 12.
    Langer, E.: The illusion of control. Journal of Personality and Social Psychology 32(2), 311 (1975)CrossRefGoogle Scholar
  13. 13.
    Tractinsky, N., Katz, A., Ikar, D.: What is beautiful is usable. Interacting With Computers 13(2), 127–145 (2000)CrossRefGoogle Scholar
  14. 14.
    Norman, D.: Emotion & design: Attractive things work better. Interactions 9(4), 36–42 (2002)CrossRefGoogle Scholar
  15. 15.
    Hakvoort, G., Gürkök, H., Plass-Oude Bos, D., Obbink, M., Poel, M.: Measuring immersion and affect in a brain-computer interface game. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M. (eds.) INTERACT 2011, Part I. LNCS, vol. 6946, pp. 115–128. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    van de Laar, B., Gürkök, H., Plass-Oude Bos, D., Nijboer, F., Nijholt, A.: Brain-computer interfaces and user experience evaluation. In: Allison, B.Z., Dunne, S., Leeb, R., Del, R., Millán, J., Nijholt, A. (eds.) Towards Practical Brain-Computer Interfaces, pp. 223–237. Springer (2012)Google Scholar
  17. 17.
    Quek, M., Boland, D., Williamson, J., Murray-Smith, R., Tavella, M., Perdikis, S., Schreuder, M., Tangermann, M.: Simulating the feel of brain-computer interfaces for design, development and social interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2011, pp. 25–28. ACM (2011)Google Scholar
  18. 18.
    Cincotti, F., Kauhanen, L., Aloise, F., Palomäki, T., Caporusso, N., Jylänki, P., Mattia, D., Babiloni, F., Vanacker, G., Nuttin, M., et al.: Vibrotactile feedback for brain-computer interface operation. Computational Intelligence and Neuroscience 2007 (2007)Google Scholar
  19. 19.
    Ware, M., McCullagh, P., McRoberts, A., Lightbody, G., Nugent, C., McAllister, G., Mulvenna, M., Thomson, E., Martin, S.: Contrasting levels of accuracy in command interaction sequences for a domestic brain-computer interface using SSVEP. In: Biomedical Engineering Conference, pp. 150–153. IEEE (2010)Google Scholar
  20. 20.
    Carlson, T., Monnard, G., Millán, J.: Vision-based shared control for a BCI wheelchair. International Journal of Bioelectromagnetism 13(1), 20–21 (2011)Google Scholar
  21. 21.
    Quek, M., Höhne, J., Murray-Smith, R., Tangermann, M.: Designing future bcis: Beyond the bit rate. In: Allison, B.Z., Dunne, S., Leeb, R., Del R. Millán, J., Nijholt, A. (eds.) Towards Practical Brain-Computer Interfaces, pp. 173–196. Springer (2012)Google Scholar
  22. 22.
    Gaver, W.W., Beaver, J., Benford, S.: Ambiguity as a resource for design. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 233–240. ACM (2003)Google Scholar
  23. 23.
    MacKay, D.J.: Information theory, inference and learning algorithms. Cambridge university press (2003)Google Scholar
  24. 24.
    Wewers, M., Lowe, N.: A critical review of visual analogue scales in the measurement of clinical phenomena. Research in Nursing & Health 13(4), 227–236 (2007)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2014

Authors and Affiliations

  • Danny Plass-Oude Bos
    • 1
  • Bram van de Laar
    • 1
  • Boris Reuderink
    • 1
  • Mannes Poel
    • 1
  • Anton Nijholt
    • 1
  1. 1.Human Media Interaction GroupUniversity of TwenteEnschedeThe Netherlands

Personalised recommendations