Gaming controlling via brain-computer interface using multiple physiological signals

Chen, SA; Chen, CH; Lin, JW; Ko, LW; Lin, CT

Gaming controlling via brain-computer interface using multiple physiological signals

Chen, SA Chen, CH Lin, JW Ko, LW Lin, CT

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Publication Type:: Conference Proceeding
Citation:: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2014, 2014-January (January), pp. 3156 - 3159
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Chen, SA	en_US
dc.contributor.author	Chen, CH	en_US
dc.contributor.author	Lin, JW	en_US
dc.contributor.author	Ko, LW	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2014, 2014-January (January), pp. 3156 - 3159	en_US
dc.identifier.issn	1062-922X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119955
dc.description.abstract	© 2014 IEEE. using physiological signals to control braincomputer interface (BCI) becomes more popular. Among many kinds of physiological signals, Electrooculography (EOG) signal is more stable which can be used to control BCI systems based on eye movement detection and signal processing methods. Also, the use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. In this paper, we described a signal processing method, which uses a wireless EEG-based BCI system designed to be worn near forehead that can detect both EEG and EOG signals, for detecting eye movements to have 9 direction controls (via EOG) and one action of execution (via EEG). This system included a wireless EEG signal acquisition device, a mechanism that can be worn stably, and an application program (APP) with signal processing algorithms. This algorithm and its classification procedure provided an effective method for identifying eye movements and attention. Finally, we designed a baseball game to test the BCI system. The results demonstrated that player can control the game well with high accuracy.	en_US
dc.relation.ispartof	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics	en_US
dc.relation.isbasedon	10.1109/smc.2014.6974413	en_US
dc.title	Gaming controlling via brain-computer interface using multiple physiological signals	en_US
dc.type	Conference Proceeding
utslib.citation.volume	January	en_US
utslib.citation.volume	2014-January	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	January	en_US
pubs.publication-status	Published	en_US
pubs.volume	2014-January	en_US

Abstract:

© 2014 IEEE. using physiological signals to control braincomputer interface (BCI) becomes more popular. Among many kinds of physiological signals, Electrooculography (EOG) signal is more stable which can be used to control BCI systems based on eye movement detection and signal processing methods. Also, the use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. In this paper, we described a signal processing method, which uses a wireless EEG-based BCI system designed to be worn near forehead that can detect both EEG and EOG signals, for detecting eye movements to have 9 direction controls (via EOG) and one action of execution (via EEG). This system included a wireless EEG signal acquisition device, a mechanism that can be worn stably, and an application program (APP) with signal processing algorithms. This algorithm and its classification procedure provided an effective method for identifying eye movements and attention. Finally, we designed a baseball game to test the BCI system. The results demonstrated that player can control the game well with high accuracy.

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http://hdl.handle.net/10453/119955