Real-Time Control of a Video Game Using Eye Movements and Two Temporal EEG Sensors

Overview

Journal Comput Intell Neurosci

Specialty Biology

Date 2015 Dec 23

PMID 26690500

Citations 10

Authors

Abdelkader Nasreddine Belkacem

Supat Saetia

Kalanyu Zintus-art

Duk Shin

Hiroyuki Kambara

Natsue Yoshimura

Nasreddine Berrached

Yasuharu Koike

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Brain-computer interface for robot control with eye artifacts for assistive applications.

Karas K, Pozzi L, Pedrocchi A, Braghin F, Roveda L Sci Rep. 2023; 13(1):17512.

PMID: 37845318 PMC: 10579221. DOI: 10.1038/s41598-023-44645-y.

Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces.

Portillo-Lara R, Tahirbegi B, Chapman C, Goding J, Green R APL Bioeng. 2021; 5(3):031507.

PMID: 34327294 PMC: 8294859. DOI: 10.1063/5.0047237.

Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients.

Belkacem A, Jamil N, Palmer J, Ouhbi S, Chen C Front Neurosci. 2020; 14:692.

PMID: 32694979 PMC: 7339951. DOI: 10.3389/fnins.2020.00692.

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface.

Shao L, Zhang L, Belkacem A, Zhang Y, Chen X, Li J J Healthc Eng. 2020; 2020:6968713.

PMID: 32399166 PMC: 7201509. DOI: 10.1155/2020/6968713.

Cognitive Behavior Classification From Scalp EEG Signals.

Dvorak D, Shang A, Abdel-Baki S, Suzuki W, Fenton A IEEE Trans Neural Syst Rehabil Eng. 2018; 26(4):729-739.

PMID: 29641377 PMC: 7970582. DOI: 10.1109/TNSRE.2018.2797547.

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