Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 12, pp 15929–15949 | Cite as

Development of a neuro-feedback game based on motor imagery EEG

  • Chenguang YangEmail author
  • Yuhang Ye
  • Xinyang Li
  • Ruowei Wang
Article

Abstract

Electroencephalogram (EEG) has widely been used to monitor subjects/patients’ mental states. Using the monitor results as feedback, neuro-feedback enables patients to learn to regulate their physiological and psychological states so that improvements in physical and psychological subjects/patients’ states could be achieved. By analyzing EEG components generated by motor imagery, a mind-controlled game based on motor imagery is developed, including the design of BCI and the design of the video game. In the game, neuro-feedback is realized to in a visual manner, through which the users could learn to improve attention span. Based on motor imagery, EEG signal is classified into two categories, the left and right hand motor imagery. The accuracy of classification is up to 70%. The bandpower analysis results show that users’ attention level improves during the experiment. In this neuro-feedback game system, EEG signal is not only used for monitoring but also used for game control. The game provides an attention state measurements for users. With the neuro-feedback in the BCI, the user and the game form a close loop interactively. The proposed BCI video game could not only be used for entertainment and relaxation purpose, but attention-span training purpose.

Keywords

EEG Neuro-feedback BCI Motor imagery Video game Attention 

Notes

Acknowledgements

This work was partially supported by National Nature Science Foundation (NSFC) under Grant 61473120, Guangdong Provincial Natural Science Foundation 2014A030313266 and International Science and Technology Collaboration Grant 2015A050502017, Science and Technology Planning Project of Guangzhou 201607010006, State Key Laboratory of Robotics and System (HIT) Grant SKLRS-2017-KF-13, and the Fundamental Research Funds for the Central Universities.

References

  1. 1.
    Aghaei AS, Mahanta M, Plataniotis KN (2015) Separable common spatio-spectral patterns for motor imagery bci systems. IEEE Trans Biomed Eng PP (99):1–1Google Scholar
  2. 2.
    Ang KK, Guan C (2015) Braincomputer interface for neuro-rehabilitation of upper limb after stroke. Proc IEEE 103:944–953CrossRefGoogle Scholar
  3. 3.
    Angelidis A, van der Does W, Schakel L, Putman P (2016) Frontal eeg theta/beta ratio as an electrophysiological marker for attentional control and its test-retest reliability. Biol Psychol 121:49–52CrossRefGoogle Scholar
  4. 4.
    Bisson E, Contant B, Sveistrup H, Lajoie Y (2007) Functional balance and dual-task reaction times in older adults are improved by virtual reality and biofeedback training. Cyberpsychol Behav 10(1):16–23CrossRefGoogle Scholar
  5. 5.
    Bos DP-O, Reuderink B, van de Laar B, Gurkok H, Muhl C, Poel M, Heylen D, Nijholt A (2010) Human-computer interaction for bci games: usability and user experience. In: 2010 International Conference on Cyberworlds (CW). IEEE, pp 277–281Google Scholar
  6. 6.
    Chan AS, Han YM, Cheung MC (2008) Electroencephalographic (eeg) measurements of mindfulness-based triarchic body-pathway relaxation technique: a pilot study. Appl Psychophysiol Biofeedback 33(1):39–47CrossRefGoogle Scholar
  7. 7.
    Cho BH, Lee J-M, Ku J, Jang DP, Kim J, Kim I-Y, Lee J-H, Kim SI (2002) Attention enhancement system using virtual reality and eeg biofeedback. In: 2002 IEEE Proceedings of Virtual reality. IEEE, pp 156–163Google Scholar
  8. 8.
    Edelman BJ, Baxterand B, He B (2015) Eeg source imaging enhances the decoding of complex right hand motor imagery tasks. IEEE Trans Biomed Eng PP (99):1–1Google Scholar
  9. 9.
    Egner T, Gruzelier JH (2001) Learned self-regulation of eeg frequency components affects attention and event-related brain potentials in humans. Neuroreport 12(18):4155–4159CrossRefGoogle Scholar
  10. 10.
    Gerkinga JM, Pfurtscheller G, Flyvbjergc H (1999) Designing optimal spatial filters for single-trial EEG classiffication in a movement task. Clin Neurophysiol 110:787–798CrossRefGoogle Scholar
  11. 11.
    Goldman LS, Genel M, Bezman RJ, Slanetz PJ et al (1998) Diagnosis and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Jama 279(14):1100–1107CrossRefGoogle Scholar
  12. 12.
    Hock AG (2000) Biofeedback system for sensing body motion and flexure, uS Patent 6,032,530Google Scholar
  13. 13.
    Holzinger A, Bruschi M, Eder W (2013) On interactive data visualization of physiological low-cost-sensor data with focus on mental stress. Springer, BerlinCrossRefGoogle Scholar
  14. 14.
    Holzinger A, Plass M, Holzinger K, Crişan GC, Pintea CM, Palade V (2016) Towards interactive machine learning (iml): applying ant colony algorithms to solve the traveling salesman problem with the human-in-the-loop approach. In: International Conference on Availability, Reliability, and Security, pp 81–95Google Scholar
  15. 15.
    Koles ZJ (1991) The quantitative extraction and toporraphic mapping of the abnormal components in the clinical EEG. Electroencephalogr Clin Neurophysiol 79:440–447CrossRefGoogle Scholar
  16. 16.
    Li X, Guan C, Zhang H, Ang KK, Ong SH (2014) Adaptation of motor imagery EEG classification model based on tensor decomposition. J Neural Eng 11:056020CrossRefGoogle Scholar
  17. 17.
    Li X, Zhang H, Guan C, Ong SH, Ang KK, Pan Y (2013) Discriminative learning of propagation and spatial pattern for motor imagery EEG analysis. Neural Comput 25(10):2709–2733MathSciNetCrossRefGoogle Scholar
  18. 18.
    Lim CG, Lee TS, Guan C, Fung DSS, Cheung YB, Teng SS, Zhang H, Krishnan KR (2010) Effectiveness of a brain-computer interface based programme for the treatment of adhd: a pilot study. Psychol Bull 43(1):73–82Google Scholar
  19. 19.
    Lim CG, Lee TS, Guan C, Fung DSS, Zhao Y, Teng SS, Zhang H, Krishnan KR (2012) A brain-computer interface based attention training program for treating attention deficit hyperactivity disorder. PLoS ONE 7(10):e46692CrossRefGoogle Scholar
  20. 20.
    Lubar JF (1991) Discourse on the development of eeg diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback Self Regul 16(3):201–225CrossRefGoogle Scholar
  21. 21.
    Nijholt A (2008) Bci for games: a state of the artsurvey. In: International Conference on Entertainment Computing. Springer, pp 225–228Google Scholar
  22. 22.
    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12(Oct):2825–2830MathSciNetzbMATHGoogle Scholar
  23. 23.
    Schoneveld EA, Malmberg M, Lichtwarck-Aschoff A, Verheijen GP, Engels RCME, Granic I (2016) A neurofeedback video game (mindlight ) to prevent anxiety in children: a randomized controlled trial. Comput Hum Behav 63:321–333CrossRefGoogle Scholar
  24. 24.
    Sharma A, Singh M (2015) Assessing alpha activity in attention and relaxed state: an eeg analysis. In: International Conference on Next Generation Computing Technologies, pp 508–513Google Scholar
  25. 25.
    Thomas KP, Vinod AP (2017) A study on the impact of neurofeedback in eeg based attention-driven game. In: IEEE International Conference on Systems, Man, and CyberneticsGoogle Scholar
  26. 26.
    Thomas KP, Vinod AP, Guan C (2013) Design of an online eeg based neurofeedback game for enhancing attention and memory. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp 433–436Google Scholar
  27. 27.
    Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM et al (2000) Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8(2):164–173CrossRefGoogle Scholar
  28. 28.
    Xinyang LI (2014) Modelling and Classification of Motor Imagery EEG for BCI[J]. Ph DGoogle Scholar
  29. 29.
    Ying J, Jiang D, Mu Z, Hu J (2008) Design and application of brain computer interface auxiliary game platform based on motor imagery. Zhongguo Zuzhi Gongcheng Yanjiu yu Linchuang Kangfu 12(35):6839–6843Google Scholar
  30. 30.
    Young BM, Nigogosyan Z, Nair VA, Walton LM, Song J, Tyler ME, Edwards DF, Caldera K, Sattin JA, Williams JC et al Case report: post-stroke interventional bci rehabilitation in an individual with preexisting sensorineural disability, Interaction of BCI with the underlying neurological conditions in patients: pros and consGoogle Scholar
  31. 31.
    Yuan H, Bose A Classifying eeg patterns during motor imageryGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK

Personalised recommendations