Genetic feature selection in EEG-based motion sickness estimation

Wei, CS; Ko, LW; Chuang, SW; Jung, TP; Lin, CT

Genetic feature selection in EEG-based motion sickness estimation

Wei, CS Ko, LW Chuang, SW Jung, TP Lin, CT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2011, pp. 365 - 369
Issue Date:: 2011-10-24

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Field	Value	Language
dc.contributor.author	Wei, CS	en_US
dc.contributor.author	Ko, LW	en_US
dc.contributor.author	Chuang, SW	en_US
dc.contributor.author	Jung, TP	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2011-10-24	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2011, pp. 365 - 369	en_US
dc.identifier.isbn	9781457710865	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119501
dc.description.abstract	Motion sickness is a common symptom that occurs when the brain receives conflicting information about the sensation of movement. Many motion sickness biomarkers have been identified, and electroencephalogram (EEG)-based motion sickness level estimation was found feasible in our previous study. This study employs genetic feature selection to find a subset of EEG features that can further improve estimation performance over the correlation-based method reported in the previous studies. The features selected by genetic feature selection were very different from those obtained by correlation analysis. Results of this study demonstrate that genetic feature selection is a very effective method to optimize the estimation of motion-sickness level. This demonstration could lead to a practical system for noninvasive monitoring of the motion sickness of individuals in real-world environments. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2011.6033244	en_US
dc.title	Genetic feature selection in EEG-based motion sickness estimation	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:

Motion sickness is a common symptom that occurs when the brain receives conflicting information about the sensation of movement. Many motion sickness biomarkers have been identified, and electroencephalogram (EEG)-based motion sickness level estimation was found feasible in our previous study. This study employs genetic feature selection to find a subset of EEG features that can further improve estimation performance over the correlation-based method reported in the previous studies. The features selected by genetic feature selection were very different from those obtained by correlation analysis. Results of this study demonstrate that genetic feature selection is a very effective method to optimize the estimation of motion-sickness level. This demonstration could lead to a practical system for noninvasive monitoring of the motion sickness of individuals in real-world environments. © 2011 IEEE.

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http://hdl.handle.net/10453/119501