TY - JOUR
T1 - Real-Time Control of a Video Game Using Eye Movements and Two Temporal EEG Sensors
AU - Belkacem, Abdelkader Nasreddine
AU - Saetia, Supat
AU - Zintus-Art, Kalanyu
AU - Shin, Duk
AU - Kambara, Hiroyuki
AU - Yoshimura, Natsue
AU - Berrached, Nasreddine
AU - Koike, Yasuharu
N1 - Publisher Copyright:
© 2015 Abdelkader Nasreddine Belkacem et al.
PY - 2015
Y1 - 2015
N2 - EEG-controlled gaming applications range widely from strictly medical to completely nonmedical applications. Games can provide not only entertainment but also strong motivation for practicing, thereby achieving better control with rehabilitation system. In this paper we present real-time control of video game with eye movements for asynchronous and noninvasive communication system using two temporal EEG sensors. We used wavelets to detect the instance of eye movement and time-series characteristics to distinguish between six classes of eye movement. A control interface was developed to test the proposed algorithm in real-time experiments with opened and closed eyes. Using visual feedback, a mean classification accuracy of 77.3% was obtained for control with six commands. And a mean classification accuracy of 80.2% was obtained using auditory feedback for control with five commands. The algorithm was then applied for controlling direction and speed of character movement in two-dimensional video game. Results showed that the proposed algorithm had an efficient response speed and timing with a bit rate of 30 bits/min, demonstrating its efficacy and robustness in real-time control.
AB - EEG-controlled gaming applications range widely from strictly medical to completely nonmedical applications. Games can provide not only entertainment but also strong motivation for practicing, thereby achieving better control with rehabilitation system. In this paper we present real-time control of video game with eye movements for asynchronous and noninvasive communication system using two temporal EEG sensors. We used wavelets to detect the instance of eye movement and time-series characteristics to distinguish between six classes of eye movement. A control interface was developed to test the proposed algorithm in real-time experiments with opened and closed eyes. Using visual feedback, a mean classification accuracy of 77.3% was obtained for control with six commands. And a mean classification accuracy of 80.2% was obtained using auditory feedback for control with five commands. The algorithm was then applied for controlling direction and speed of character movement in two-dimensional video game. Results showed that the proposed algorithm had an efficient response speed and timing with a bit rate of 30 bits/min, demonstrating its efficacy and robustness in real-time control.
UR - http://www.scopus.com/inward/record.url?scp=84948783708&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84948783708&partnerID=8YFLogxK
U2 - 10.1155/2015/653639
DO - 10.1155/2015/653639
M3 - Article
C2 - 26690500
AN - SCOPUS:84948783708
SN - 1687-5265
VL - 2015
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 653639
ER -