Electrooculogram-based virtual reality game control using blink detection and gaze calibration

This paper describes an Electrooculogram (EOG) and gaze based hands-free natural interaction system design for virtual reality (VR) games which enhances the immersive VR experience. The traditional interfaces like joysticks, mouse, keyboards, hand-worn data-gloves when used with VR HMD peripherals are obtrusive for virtual experience. This is a step further towards building see-and-play user interaction in VR games. This natural interface provides an enhanced gaming experience in which the user's virtual environment is interacting as per the eye movements. Electrooculogram based online eye blink detection and gaze calibration were carried out with average efficiency of 96% and 80% respectively. Based on the calibrated eye movements, the virtual environment in the “VRrailSurfer” is adjusted and interactions with the virtual game objects are carried out. Ten subjects (8 male, 2 female) were asked to play 5 trails of our prototype VR game “VRrailSurfer”. The average real-time game control accuracy across subjects was found to be 78%. The feasibility of obtaining EOG based VR game controls and the subjective analysis of user's immersive VR experience are also discussed.

[1]  Serkan Gurkan,et al.  Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard , 2010, IEEE Transactions on Instrumentation and Measurement.

[2]  Elif Derya Übeyli,et al.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007, IEEE Trans. Inf. Technol. Biomed..

[3]  G. Lewis,et al.  Virtual reality games for rehabilitation of people with stroke: perspectives from the users , 2011, Disability and rehabilitation. Assistive technology.

[4]  Thomas Olofsson,et al.  Credibility and applicability of virtual reality models in design and construction , 2008, Adv. Eng. Informatics.

[5]  Roy Ruddle.  Review: 3D User Interfaces: Theory and Practice Doug A. Bowman , Ernst Kruijff , Joseph J. LaViola Jr. , Ivan Poupyrev , 2005 .

[6]  Chun-Liang Hsu,et al.  EOG-based Human-Computer Interface system development , 2010, Expert Syst. Appl..

[7]  M. Mazo,et al.  System for assisted mobility using eye movements based on electrooculography , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Marcelo Knörich Zuffo,et al.  On the usability of gesture interfaces in virtual reality environments , 2005, CLIHC '05.

[9]  Andrew T. Duchowski,et al.  Eye Tracking Methodology: Theory and Practice , 2003, Springer London.

[10]  Michitaka Hirose,et al.  Brain-Computer Interfaces, Virtual Reality, and Videogames , 2008, Computer.

[11]  John R. Smith,et al.  Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment , 2005, EURASIP J. Adv. Signal Process..

[12]  Elif Derya Übeyli,et al.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007, IEEE Transactions on Information Technology in Biomedicine.

[13]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface (BBCI) – towards a new communication channel for online control in gaming applications , 2007, Multimedia Tools and Applications.

[14]  David E Thompson,et al.  Performance assessment in brain-computer interface-based augmentative and alternative communication , 2013, BioMedical Engineering OnLine.

[15]  Masaki Hayashi,et al.  Implementation of EOG-based Gaze Estimation in HMD with Head-tracker , 2008 .

[16]  Manuel Mazo,et al.  Wheelchair Guidance Strategies Using EOG , 2002, J. Intell. Robotic Syst..

[17]  Christian Callegari,et al.  Advances in Computing, Communications and Informatics (ICACCI) , 2015 .

[18]  Yu-Te Wang,et al.  Online Voluntary Eye Blink Detection using Electrooculogram , 2014 .