A Hybrid Physiological Approach of Emotional Reaction Detection Using Combined FCM and SVM Classifier

Guo, K; Yu, H; Chai, R; Nguyen, H; Su, SW

A Hybrid Physiological Approach of Emotional Reaction Detection Using Combined FCM and SVM Classifier

Guo, K Yu, H

Chai, R

Nguyen, H

Su, SW

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019, pp. 7088 - 7091
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Guo, K	en_US
dc.contributor.author	Yu, H https://orcid.org/0000-0003-3018-064X	en_US
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024	en_US
dc.contributor.author	Nguyen, H https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2019-07-01	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019, pp. 7088 - 7091	en_US
dc.identifier.isbn	9781538613115	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138250
dc.description.abstract	© 2019 IEEE. Users' emotional reaction capturing is one of the primary issues for brain computer interface applications. Despite the intuitive feedback provided by the qualitative methods, emotional reactions are expected to be detected and classified quantitatively. Based on the human emotion representation on physiological signal, this paper offers an hybrid approach combining electroencephalogram (EEG) and facial expression together to classify the human emotion. Several advanced signal processing techniques are used to simplify the data and extract the features involving local binary patterns (LBP), Compressed Sensing (CS) and Wavelet Transform (WT). A novel machine learning algorithm, combined Fuzzy Cognitive Maps (FCM) and Support Vector Machine (SVM) are implemented to recognise the feature patterns. The result illustrates a stable emotion classification system with 75.64% accuracy. This design can provide fast and precise emotional feedback, which would further improve the communication between human and computer.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2019.8857698	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Hybrid Physiological Approach of Emotional Reaction Detection Using Combined FCM and SVM Classifier	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2019 IEEE. Users' emotional reaction capturing is one of the primary issues for brain computer interface applications. Despite the intuitive feedback provided by the qualitative methods, emotional reactions are expected to be detected and classified quantitatively. Based on the human emotion representation on physiological signal, this paper offers an hybrid approach combining electroencephalogram (EEG) and facial expression together to classify the human emotion. Several advanced signal processing techniques are used to simplify the data and extract the features involving local binary patterns (LBP), Compressed Sensing (CS) and Wavelet Transform (WT). A novel machine learning algorithm, combined Fuzzy Cognitive Maps (FCM) and Support Vector Machine (SVM) are implemented to recognise the feature patterns. The result illustrates a stable emotion classification system with 75.64% accuracy. This design can provide fast and precise emotional feedback, which would further improve the communication between human and computer.

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http://hdl.handle.net/10453/138250