Tennis computer game with brain control using EEG signals

This paper presents a game designed to improve the quality of life of people with severe disabilities for using a computer mouse or keyboard. The game has been designed using biofeedback and BCI technology included in a machine learning algorithm and integrated into a game interface using the SDL library. The results have been entirely satisfactory because the success rate in trained patients is over the 80% and the game has been qualified by them as friendly. Next step will be to develop the graphics in 3D instead of 2D. The social benefits of this type of application can affect diseases involving movement difficulties for controlling keyboard and mouse of a computer, or even rare diseases like congenital double athetosis.

[1]  Cuntai Guan,et al.  Introduction to NeuroComm: a Platform for Developing Real-Time EEG-based Brain-Computer Interface Applications , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Mincheol Whang,et al.  A brain–computer interface method combined with eye tracking for 3D interaction , 2010, Journal of Neuroscience Methods.

[3]  F. Babiloni,et al.  Neuroelectrical source imaging of mu rhythm control for BCI applications , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Kevin Hawkins,et al.  Beginning OpenGL Game Programming , 2004 .

[5]  J. Pineda,et al.  Learning to control brain rhythms: making a brain-computer interface possible , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[7]  G. V. Haines,et al.  Logarithmic Fourier transformation , 1988 .

[8]  C. Sidney Burrus,et al.  Generalized digital Butterworth filter design , 1998, IEEE Trans. Signal Process..

[9]  Dinesh Talwar.  Primer of EEG with a Mini-Atlas , 2004 .

[10]  D. Pidhayny,et al.  The origins of feedback control , 1972 .

[11]  G. D. Bergland,et al.  A guided tour of the fast Fourier transform , 1969, IEEE Spectrum.

[12]  Goutam Chakraborty,et al.  Clustering of EEG data using maximum entropy method and LVQ , 2010 .

[13]  Alan T. Pope,et al.  Helping Video Games Rewire "Our Minds" , 2001 .

[14]  L.J. Trejo,et al.  Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Liu Mingyu,et al.  Development of EEG Biofeedback System Based on Virtual Reality Environment , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.