Virtual game scenario generation using brain computer interface

In this paper, we have proposed an effective method to control simulated foggy scene or racing car speed by using brain computer interface (BCI). Our method benefits much from the attention level value computed by the Thinkgear chip in Neurosky’s MindWave headset. This EEG sensor, together with the atmospheric scatting model or the 2D game framework, is applied to control the fog density or the car speed of the virtual scenario. For the model-based foggy scenario simulation, virtual foggy scene is obtained by taking the predefined airlight and the transmission map into the atmospheric scattering model. For the software-based racing scenario simulation, the moving car is created by using the software framework Sprite and its speed is controlled by the EEG sensor data. Both methods are dominated by few parameters and can be used in computer games, mental strength recovery, concentration training and many other educational environments. The Experimental results show that the proposed model-based method and software-based method may generate quite visually pleasing game scenario with good sense of reality and immersion.

[1]  Xiaoou Tang,et al.  Single Image Haze Removal Using Dark Channel Prior , 2011 .

[2]  Szczepan Paszkiel Augmented Reality of Technological Environment in Correlation with Brain Computer Interfaces for Control Processes , 2014, Recent Advances in Automation, Robotics and Measuring Techniques.

[3]  B. He,et al.  A High Resolution EEG Study of Dynamic Brain Activity during Video Game Play , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Sriparna Saha,et al.  A novel gesture driven fuzzy interface system for car racing game , 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[5]  Fan Guo,et al.  Foggy Scene Rendering Based on Transmission Map Estimation , 2014, Int. J. Comput. Games Technol..

[6]  Roman Szewczyk,et al.  Recent Advances in Automation, Robotics and Measuring Techniques , 2014, Recent Advances in Automation, Robotics and Measuring Techniques.

[7]  Bao-Liang Lu,et al.  EEG-based emotion recognition during watching movies , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

[8]  Fan Guo,et al.  Virtual fog scene simulation based on brain computer interface , 2016 .

[9]  Shree K. Nayar,et al.  A practical analytic single scattering model for real time rendering , 2005, SIGGRAPH '05.

[10]  M. V. Rossum,et al.  Multiple scattering of classical waves: microscopy, mesoscopy, and diffusion , 1998, cond-mat/9804141.

[11]  Yuan-Pin Lin,et al.  EEG-Based Emotion Recognition in Music Listening , 2010, IEEE Transactions on Biomedical Engineering.

[12]  Koo-Hyoung Lee,et al.  Evaluation of Attention and Relaxation Levels of Archers in Shooting Process using Brain Wave Signal Analysis Algorithms , 2009 .

[13]  Zhangye Wang,et al.  Real‐time rendering of sky scene considering scattering and refraction , 2007, Comput. Animat. Virtual Worlds.

[14]  Jean-Philippe Tarel,et al.  Vision Enhancement in Homogeneous and Heterogeneous Fog , 2012, IEEE Intelligent Transportation Systems Magazine.

[15]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Nelson L. Max,et al.  Atmospheric illumination and shadows , 1986, SIGGRAPH.

[17]  Yoshinori Dobashi,et al.  Display of clouds taking into account multiple anisotropic scattering and sky light , 1996, SIGGRAPH.

[18]  Bertram Walter,et al.  Modeling and Rendering of the Atmosphere Using Mie‐Scattering , 1997, Comput. Graph. Forum.

[19]  John Quarles,et al.  Exercise-based interaction techniques for a virtual reality car racing game , 2012, 2012 IEEE Virtual Reality Workshops (VRW).