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


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.


EEG Neuro-feedback BCI Motor imagery Video game Attention 



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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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

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