EOG-Based Eye Movement Classification and Application on HCI Baseball Game

Lin, CT; King, JT; Bharadwaj, P; Chen, CH; Gupta, A; Ding, W; Prasad, M

EOG-Based Eye Movement Classification and Application on HCI Baseball Game

Lin, CT

King, JT Bharadwaj, P Chen, CH Gupta, A Ding, W Prasad, M

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Publication Type:: Journal Article
Citation:: IEEE Access, 2019, 7 pp. 96166 - 96176
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	King, JT	en_US
dc.contributor.author	Bharadwaj, P	en_US
dc.contributor.author	Chen, CH	en_US
dc.contributor.author	Gupta, A	en_US
dc.contributor.author	Ding, W	en_US
dc.contributor.author	Prasad, M	en_US
dc.date.available	2021-01-01T18:03:35Z
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEEE Access, 2019, 7 pp. 96166 - 96176	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136592
dc.description.abstract	© 2013 IEEE. Electrooculography (EOG) is considered as the most stable physiological signal in the development of human-computer interface (HCI) for detecting eye-movement variations. EOG signal classification has gained more traction in recent years to overcome physical inconvenience in paralyzed patients. In this paper, a robust classification technique, such as eight directional movements is investigated by introducing a concept of buffer along with a variation of the slope to avoid misclassification effects in EOG signals. Blinking detection becomes complicated when the magnitude of the signals are considered. Hence, a correction technique is introduced to avoid misclassification for oblique eye movements. Meanwhile, a case study has been considered to apply these correction techniques to HCI baseball game to learn eye-movements.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2019.2927755	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	EOG-Based Eye Movement Classification and Application on HCI Baseball Game	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

© 2013 IEEE. Electrooculography (EOG) is considered as the most stable physiological signal in the development of human-computer interface (HCI) for detecting eye-movement variations. EOG signal classification has gained more traction in recent years to overcome physical inconvenience in paralyzed patients. In this paper, a robust classification technique, such as eight directional movements is investigated by introducing a concept of buffer along with a variation of the slope to avoid misclassification effects in EOG signals. Blinking detection becomes complicated when the magnitude of the signals are considered. Hence, a correction technique is introduced to avoid misclassification for oblique eye movements. Meanwhile, a case study has been considered to apply these correction techniques to HCI baseball game to learn eye-movements.

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http://hdl.handle.net/10453/136592