EEG-based motion sickness classification system with genetic feature selection

Ko, LW; Lee, HC; Tsai, SF; Shih, TC; Chuang, YT; Huang, HL; Ho, SY; Lin, CT

EEG-based motion sickness classification system with genetic feature selection

Ko, LW Lee, HC Tsai, SF Shih, TC Chuang, YT Huang, HL Ho, SY Lin, CT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, 2013, pp. 158 - 164
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Ko, LW	en_US
dc.contributor.author	Lee, HC	en_US
dc.contributor.author	Tsai, SF	en_US
dc.contributor.author	Shih, TC	en_US
dc.contributor.author	Chuang, YT	en_US
dc.contributor.author	Huang, HL	en_US
dc.contributor.author	Ho, SY	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, 2013, pp. 158 - 164	en_US
dc.identifier.isbn	9781467358712	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119837
dc.description.abstract	People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Many previous studies indicated that motion sickness sometimes led to traffic accidents, so it becomes an important issue in our daily life. In this study, we designed a VR-based motion-sickness platform with a 32-channel EEG system and a joystick which is used to report the motion sickness level (MSL) in real time during experiments. The results show it is feasible to estimate subject's MSL based on re-sampling frequency band proved by the high test accuracy. A comparison between general prediction models (such as LDA, QDA, KNN) and IBCGA shows that the IBCGA can be effectively increase the accuracy. In this paper, an extended-IBCGA (e-IBCGA) is proposed and it provides more accuracy than the prior-art research. The test results show that e-IBCGA increases at least 10% to 20% test accuracy in 6 subjects. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013	en_US
dc.relation.isbasedon	10.1109/CCMB.2013.6609180	en_US
dc.title	EEG-based motion sickness classification system with genetic feature selection	en_US
dc.type	Conference Proceeding
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.publication-status	Published	en_US

Abstract:

People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Many previous studies indicated that motion sickness sometimes led to traffic accidents, so it becomes an important issue in our daily life. In this study, we designed a VR-based motion-sickness platform with a 32-channel EEG system and a joystick which is used to report the motion sickness level (MSL) in real time during experiments. The results show it is feasible to estimate subject's MSL based on re-sampling frequency band proved by the high test accuracy. A comparison between general prediction models (such as LDA, QDA, KNN) and IBCGA shows that the IBCGA can be effectively increase the accuracy. In this paper, an extended-IBCGA (e-IBCGA) is proposed and it provides more accuracy than the prior-art research. The test results show that e-IBCGA increases at least 10% to 20% test accuracy in 6 subjects. © 2013 IEEE.

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http://hdl.handle.net/10453/119837