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EEG correlates of video game experience and user profile in motor-imagery-based brain–computer interaction

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Abstract

Through the use of brain–computer interfaces (BCIs), neurogames have become increasingly more advanced by incorporating immersive virtual environments and 3D worlds. However, training both the user and the system requires long and repetitive trials resulting in fatigue and low performance. Moreover, many users are unable to voluntarily modulate the amplitude of their brain activity to control the neurofeedback loop. In this study, we are focusing on the effect that gaming experience has in brain activity modulation as an attempt to systematically identify the elements that contribute to high BCI control and to be utilized in neurogame design. Based on the current literature, we argue that experienced gamers could have better performance in BCI training due to enhanced sensorimotor learning derived from gaming. To investigate this, two experimental studies were conducted with 20 participants overall, undergoing 3 BCI sessions, resulting in 88 EEG datasets. Results indicate (a) an effect from both demographic and gaming experience data to the activity patterns of EEG rhythms, and (b) increased gaming experience might not increase significantly performance, but it could provide faster learning for ‘Hardcore’ gamers.

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References

  1. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol. 113(6), 767–791 (2002)

    Article  Google Scholar 

  2. Lan, Z., Sourina, O., Wang, L., Liu, Y.: Real-time EEG-based emotion monitoring using stable features. Vis. Comput. 32(3), 347–358 (2015)

    Article  Google Scholar 

  3. Škola, F., Liarokapis, F.: Examining the effect of body ownership in immersive virtual and augmented reality environments. Vis. Comput. 32(6–8), 761–770 (2016)

    Google Scholar 

  4. Wang, Y., Gao, X., Hong, B., Jia, C., Gao, S.: Brain–computer interfaces based on visual evoked potentials. IEEE Eng. Med. Biol. Mag. 27(5), 64–71 (2008)

    Article  Google Scholar 

  5. Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., Edlinger, G.: How many people are able to control a P300-based brain-computer interface (BCI)? Neurosci. Lett. 462(1), 94–98 (2009)

    Article  Google Scholar 

  6. Pfurtscheller, G., Muller-Putz, G.R., Scherer, R., Neuper, C.: Rehabilitation with brain–computer interface systems. Computer 41(10), 58–65 (2008)

    Article  Google Scholar 

  7. Tan, D., Nijholt, A.: Brain–computer interfaces and human–computer interaction. In: Tan, D.S., Nijholt, A. (eds.) Brain-Computer Interfaces, pp. 3–19. Springer, London (2010)

    Chapter  Google Scholar 

  8. Lecuyer, A., Lotte, F., Reilly, R.B., Leeb, R., Hirose, M., Slater, M.: Brain–computer interfaces, virtual reality, and videogames. Computer 41(10), 66–72 (2008)

    Article  Google Scholar 

  9. van de Laar, B., Gurkok, H., Plass-Oude Bos, D., Poel, M., Nijholt, A.: Experiencing BCI control in a popular computer game. IEEE Trans. Comput. Intell. AI Games 5(2), 176–184 (2013)

    Article  Google Scholar 

  10. Slater, M., Steed, A.: A virtual presence counter. Presence 9(5), 413–434 (2000)

    Article  Google Scholar 

  11. Facebook to buy virtual reality company Oculus for $2 billion. CBC News. Available: http://www.cbc.ca/news/technology/facebook-to-buy-oculus-virtual-reality-firm-for-2b-1.2586318. Accessed 01 Aug 2016

  12. Azuma, R.T.: A survey of augmented reality. Presence Teleoper. virtual Environ. 6(4), 355–385 (1997)

    Article  Google Scholar 

  13. Blum, T., Stauder, R., Euler, E., Navab, N.: Superman-like X-ray vision: towards brain–computer interfaces for medical augmented reality. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp 271–272 (2012)

  14. Slater, M., Wilbur, S.: A framework for immersive virtual environments (FIVE): speculations on the role of presence in virtual environments. Presence Teleoper. Virtual Environ. 6(6), 603–616 (1997)

    Article  Google Scholar 

  15. Slater, M.: Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364(1535), 3549–3557 (2009)

    Article  Google Scholar 

  16. Friedman, D., Leeb, R., Pfurtscheller, G., Slater, M.: Human–computer interface issues in controlling virtual reality with brain–computer interface. Human Comput. Interact. 25(1), 67–94 (2010)

    Article  Google Scholar 

  17. Friedman, D., Leeb, R., Guger, C., Steed, A., Pfurtscheller, G., Slater, M.: Navigating virtual reality by thought: what is it like? Presence Teleoper. Virtual Environ. 16(1), 100–110 (2007)

    Article  Google Scholar 

  18. Friedman, D.: Brain–computer interfacing and virtual reality. In: Nakatsu, R., Rauterberg, M., Ciancarini, P. (eds.) Handbook of Digital Games and Entertainment Technologies, pp. 1–22. Springer, Singapore (2015)

  19. Ahn, M., Lee, M., Choi, J., Jun, S.C.: A review of brain–computer interface games and an opinion survey from researchers, developers and users. Sensors 14(8), 14601–14633 (2014)

    Article  Google Scholar 

  20. Pineda, J.A., Silverman, D.S., Vankov, A., Hestenes, J.: Learning to control brain rhythms: making a brain–computer interface possible. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 181–184 (2003)

    Article  Google Scholar 

  21. Krepki, R., Blankertz, B., Curio, G., Müller, K.-R.: The Berlin brain–computer interface (BBCI)—towards a new communication channel for online control in gaming applications. Multimed. Tools Appl. 33(1), 73–90 (2007)

    Article  Google Scholar 

  22. Müller-Putz, G., Scherer, R., and Pfurtscheller, G.: Game-like training to learn single switch operated neuroprosthetic control. In: BRAINPLAY 07 Brain–Computer Interfaces and Games Workshop at ACE (Advances in Computer Entertainment), p. 41 (2007)

  23. Krauledat, M., Grzeska, K., Sagebaum, M., Blankertz, B., Vidaurre, C., Müller, K.-R., Schröder, M.: Playing Pinball with non-invasive BCI. In: Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 21, pp. 1641–1648. Curran Associates, Inc., (2009)

  24. Liarokapis, F., Vourvopoulos, A., Ene, A., Petridis, P.: Assessing brain–computer interfaces for controlling serious games. In: 2013 5th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES), pp. 1–4 (2013)

  25. Allison, B.Z., Neuper, C.: Could anyone use a BCI? In: Tan, D.S., Nijholt, A. (eds.) Brain-Computer Interfaces, pp. 35–54. Springer, London (2010)

    Chapter  Google Scholar 

  26. Vidaurre, C., Blankertz, B.: Towards a cure for BCI illiteracy. Brain Topogr. 23(2), 194–198 (2009)

    Article  Google Scholar 

  27. Vuckovic, A.: Motor imagery questionnaire as a method to detect BCI illiteracy. In: 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL), pp. 1–5 (2010)

  28. Guger, C., Edlinger, G., Harkam, W., Niedermayer, I., Pfurtscheller, G.: How many people are able to operate an EEG-based brain–computer interface (BCI)? IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 145–147 (2003)

    Article  Google Scholar 

  29. Neuper, C., Schlögl, A., Pfurtscheller, G.: Enhancement of left–right sensorimotor EEG differences during feedback-regulated motor imagery. J. Clin. Neurophysiol. Off. Publ. Am. Electroencephalogr. Soc. 16(4), 373–382 (1999)

    Google Scholar 

  30. Garry, M.I., Kamen, G., Nordstrom, M.A.: Hemispheric differences in the relationship between corticomotor excitability changes following a fine-motor task and motor learning. J. Neurophysiol. 91(4), 1570–1578 (2004)

    Article  Google Scholar 

  31. Marshall, D., Coyle, D., Wilson, S., Callaghan, M.: Games, gameplay, and BCI: the state of the art. IEEE Trans. Comput. Intell. AI Games 5(2), 82–99 (2013)

    Article  Google Scholar 

  32. Lotte, F., Larrue, F., Mühl, C.: Flaws in current human training protocols for spontaneous brain–computer interfaces: lessons learned from instructional design. Front. Hum. Neurosci. 7 (2013)

  33. Lotte, F.: On the need for alternative feedback training approaches for BCI. In: Presented at the Berlin Brain–Computer Interface Workshop (2012)

  34. Schomer, D.L., da Silva, F.H.L.: Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams and Wilkins (2011)

  35. Green, C.S., Bavelier, D.: Action video game modifies visual selective attention. Nature 423(6939), 534–537 (2003)

    Article  Google Scholar 

  36. Feng, J., Spence, I., Pratt, J.: Playing an action video game reduces gender differences in spatial cognition. Psychol. Sci. 18(10), 850–855 (2007)

    Article  Google Scholar 

  37. Gozli, D.G., Bavelier, D., Pratt, J.: The effect of action video game playing on sensorimotor learning: evidence from a movement tracking task. Hum. Mov. Sci. 38C, 152–162 (2014)

    Article  Google Scholar 

  38. Granek, J.A., Gorbet, D.J., Sergio, L.E.: Extensive video-game experience alters cortical networks for complex visuomotor transformations. Cortex. J. Devoted Study Nerv. Syst. Behav. 46(9), 1165–1177 (2010)

    Article  Google Scholar 

  39. Friedrich, E.V.C., Scherer, R., Neuper, C.: Long-term evaluation of a 4-class imagery-based brain–computer interface. Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol. 124(5), 916–927 (2013)

    Article  Google Scholar 

  40. 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. Off. J. Int. Fed. Clin. Neurophysiol. 119(2), 399–408 (2008)

    Article  Google Scholar 

  41. Vourvopoulos, A., Liarokapis, F., and Chen, M.: The Effect of Prior Gaming Experience in Motor Imagery Training for Brain-Computer Interfaces: A Pilot Study. In: 7th International Conference on Games and Virtual Worlds for Serious Applications (VS-Games’15), Skövde, Sweden (2015)

  42. Kalcher, J., Flotzinger, D., Neuper, C., Gölly, S., Pfurtscheller, D.G.: Graz brain–computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns. Med. Biol. Eng. Comput. 34(5), 382–388 (1996)

    Article  Google Scholar 

  43. Herbert H, Jasper MD. Report of the committee on methods of clinical examination in electroencephalography 1957. Electroencephalography Clin Neurophysiol. 10(2), 370–375 (1957). doi:10.1016/0013-4694(58)90053-1

  44. 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 Teleoper. Virtual Environ. 19(1), 35–53 (2010)

    Article  Google Scholar 

  45. Vourvopoulos, A., Faria, A.L., Cameirão, M.S., Bermúdez i Badia, S.: RehabNet: A Distributed Architecture for Motor and Cognitive Neuro-Rehabilitation. Understanding the Human Brain through Virtual Environment Interaction. In: IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom) (2013)

  46. Taylor II, R.M., Hudson, T.C., Seeger, A., Weber, H., Juliano, J., Helser, A.T.: VRPN: A Device-independent, Network-transparent VR Peripheral System. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology, New York, NY, USA, pp. 55–61 (2001)

  47. Oldfield, R.C.: The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9(1), 97–113 (1971)

    Article  Google Scholar 

  48. Roberts, R., Callow, N., Hardy, L., Markland, D., Bringer, J.: Movement imagery ability: development and assessment of a revised version of the vividness of movement imagery questionnaire. J. Sport Exerc. Psychol. 30(2), 200–221 (2008)

    Article  Google Scholar 

  49. Adams, E., Ip, B.: From Casual to Core: A Statistical Mechanism for Studying Gamer Dedication. Available http://www.gamasutra.com/view/feature/131397/from_casual_to_core_a_statistical_.php. Accessed 05 Jan 2015

  50. Jolliffe, I.: Principal Component Analysis. In: Wiley StatsRef: Statistics Reference Online. Wiley(2014)

  51. Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)

    Article  Google Scholar 

  52. Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biol. Psychol. 40(1–2), 187–195 (1995)

    Article  Google Scholar 

  53. Berka, C., Levendowski, D.J., Lumicao, M.N., Yau, A., Davis, G., Zivkovic, V.T., Olmstead, R.E., Tremoulet, P.D., Craven, P.L.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(5), B231–B244 (2007)

    Google Scholar 

  54. Galin, D., Ornstein, R., Herron, J., Johnstone, J.: Sex and handedness differences in EEG measures of hemispheric specialization. Brain Lang. 16(1), 19–55 (1982)

    Article  Google Scholar 

  55. Glass, A., Butler, S.R., Carter, J.C.: Hemispheric asymmetry of EEG alpha activation: effects of gender and familial handedness. Biol. Psychol. 19(3), 169–187 (1984)

    Article  Google Scholar 

  56. Lardon, M.T., Polich, J.: EEG changes from long-term physical exercise. Biol. Psychol. 44(1), 19–30 (1996)

    Article  Google Scholar 

  57. Klimesch, W.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29(2–3), 169–195 (1999)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the European Commission through the RehabNet project—Neuroscience-Based Interactive Systems for Motor Rehabilitation—EC (303891 RehabNet FP7-PEOPLE-2011-CIG), by the Fundação para a Ciência e Tecnologia (Portuguese Foundation for Science and Technology) through SFRH/BD/97117/2013, and LARSyS (Laboratório de Robótica e Sistemas em Engenharia e Ciência) through UID/EEA/50009/2013. Authors would also like to thank the members of NeuroRehab Lab at the University of Madeira and the HCI Lab at Masaryk University for their support and inspiration.

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Authors and Affiliations

  1. Faculdade das Ciências Exatas e da Engenharia, Universidade da Madeira, Campus Universitário da Penteada, 9020-105, Funchal, Portugal

    Athanasios Vourvopoulos & Sergi Bermudez i Badia

  2. Madeira Interactive Technologies Institute, Polo Científico e Tecnológico da Madeira, Caminho da Penteada, 9020-105, Funchal, Portugal

    Athanasios Vourvopoulos & Sergi Bermudez i Badia

  3. Human-Computer Interaction Laboratory, Masaryk University, Brno, Czech Republic

    Fotis Liarokapis

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  1. Athanasios Vourvopoulos
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Correspondence to Athanasios Vourvopoulos.

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Vourvopoulos, A., Bermudez i Badia, S. & Liarokapis, F. EEG correlates of video game experience and user profile in motor-imagery-based brain–computer interaction. Vis Comput 33, 533–546 (2017). https://doi.org/10.1007/s00371-016-1304-2

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