EEG emotion recognition using reduced channel wavelet entropy and average wavelet coefficient features with normal Mutual Information method

Candra, H; Yuwono, M; Chai, R; Nguyen, HT; Su, S

EEG emotion recognition using reduced channel wavelet entropy and average wavelet coefficient features with normal Mutual Information method

Candra, H Yuwono, M Chai, R

Nguyen, HT

Su, S

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, pp. 463 - 466
Issue Date:: 2017-09-13

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted ManuscriptAdobe PDF (634.58 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Candra, H	en_US
dc.contributor.author	Yuwono, M	en_US
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2017-09-13	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, pp. 463 - 466	en_US
dc.identifier.isbn	9781509028092	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115413
dc.description.abstract	© 2017 IEEE. Recognizing emotion from EEG signals is a complicated task that requires complex features and a substantial number of EEG channels. Simple algorithms to analyse the feature and reduce the EEG channel number will give an indispensable advantages. Therefore, this study explores a combination of wavelet entropy and average wavelet coefficient (WEAVE) as a potential EEG-emotion feature to classify valence and arousal emotions with the advantage of the ability to identify the occurrence of a pattern while at the same time identify the shape of a pattern in EEG emotion signal. The complexity of the feature was reduced using the Normalized Mutual Information (NMI) method to obtain a reduced number of channels. Classification with the WEAVE feature achieved 76.8% accuracy for valence and 74.3% for arousal emotion, respectively. The analysis with NMI shows that the WEAVE feature has linear characteristics and offers possibilities to reduce the EEG channels to a certain number. Further analysis also reveals that detection of valence emotion with reduced EEG channels has a different combination of EEG channels compared to arousal emotion.	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.2017.8036862	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Emotions	en_US
dc.subject.mesh	Arousal	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Entropy	en_US
dc.subject.mesh	Wavelet Analysis	en_US
dc.title	EEG emotion recognition using reduced channel wavelet entropy and average wavelet coefficient features with normal Mutual Information method	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical 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	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Recognizing emotion from EEG signals is a complicated task that requires complex features and a substantial number of EEG channels. Simple algorithms to analyse the feature and reduce the EEG channel number will give an indispensable advantages. Therefore, this study explores a combination of wavelet entropy and average wavelet coefficient (WEAVE) as a potential EEG-emotion feature to classify valence and arousal emotions with the advantage of the ability to identify the occurrence of a pattern while at the same time identify the shape of a pattern in EEG emotion signal. The complexity of the feature was reduced using the Normalized Mutual Information (NMI) method to obtain a reduced number of channels. Classification with the WEAVE feature achieved 76.8% accuracy for valence and 74.3% for arousal emotion, respectively. The analysis with NMI shows that the WEAVE feature has linear characteristics and offers possibilities to reduce the EEG channels to a certain number. Further analysis also reveals that detection of valence emotion with reduced EEG channels has a different combination of EEG channels compared to arousal emotion.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/115413