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.
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References
Aghaei AS, Mahanta M, Plataniotis KN (2015) Separable common spatio-spectral patterns for motor imagery bci systems. IEEE Trans Biomed Eng PP (99):1–1
Ang KK, Guan C (2015) Braincomputer interface for neuro-rehabilitation of upper limb after stroke. Proc IEEE 103:944–953
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 121:49–52
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):16–23
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 277–281
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):39–47
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 156–163
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):1–1
Egner T, Gruzelier JH (2001) Learned self-regulation of eeg frequency components affects attention and event-related brain potentials in humans. Neuroreport 12(18):4155–4159
Gerkinga JM, Pfurtscheller G, Flyvbjergc H (1999) Designing optimal spatial filters for single-trial EEG classiffication in a movement task. Clin Neurophysiol 110:787–798
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):1100–1107
Hock AG (2000) Biofeedback system for sensing body motion and flexure, uS Patent 6,032,530
Holzinger A, Bruschi M, Eder W (2013) On interactive data visualization of physiological low-cost-sensor data with focus on mental stress. Springer, Berlin
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 81–95
Koles ZJ (1991) The quantitative extraction and toporraphic mapping of the abnormal components in the clinical EEG. Electroencephalogr Clin Neurophysiol 79:440–447
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 11:056020
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):2709–2733
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):73–82
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):e46692
Lubar JF (1991) Discourse on the development of eeg diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback Self Regul 16(3):201–225
Nijholt A (2008) Bci for games: a state of the artsurvey. In: International Conference on Entertainment Computing. Springer, pp 225–228
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):2825–2830
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 63:321–333
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 508–513
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 Cybernetics
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 433–436
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):164–173
Xinyang LI (2014) Modelling and Classification of Motor Imagery EEG for BCI[J]. Ph D
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):6839–6843
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 cons
Yuan H, Bose A Classifying eeg patterns during motor imagery
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.
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Yang, C., Ye, Y., Li, X. et al. Development of a neuro-feedback game based on motor imagery EEG. Multimed Tools Appl 77, 15929–15949 (2018). https://doi.org/10.1007/s11042-017-5168-x
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DOI: https://doi.org/10.1007/s11042-017-5168-x