Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.

Li, C; Li, P; Zhang, Y; Li, N; Si, Y; Li, F; Cao, Z; Chen, H; Chen, B; Yao, D; Xu, P

Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.

Li, C Li, P Zhang, Y Li, N Si, Y Li, F Cao, Z

Chen, H Chen, B Yao, D Xu, P

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Neural Netw Learn Syst, 2023, PP, (99)
Issue Date:: 2023-02-02

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Field	Value	Language
dc.contributor.author	Li, C
dc.contributor.author	Li, P
dc.contributor.author	Zhang, Y
dc.contributor.author	Li, N
dc.contributor.author	Si, Y
dc.contributor.author	Li, F
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.contributor.author	Chen, H
dc.contributor.author	Chen, B
dc.contributor.author	Yao, D
dc.contributor.author	Xu, P
dc.date.accessioned	2024-03-26T03:19:51Z
dc.date.available	2024-03-26T03:19:51Z
dc.date.issued	2023-02-02
dc.identifier.citation	IEEE Trans Neural Netw Learn Syst, 2023, PP, (99)
dc.identifier.issn	2162-237X
dc.identifier.issn	2162-2388
dc.identifier.uri	http://hdl.handle.net/10453/177162
dc.description.abstract	Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and increase the stability of emotion recognition, we propose an effective emotion recognition model that performs multicategory emotion recognition with multiple emotion-related spatial network topology patterns (MESNPs) by learning discriminative graph topologies in EEG brain networks. To evaluate the performance of our proposed MESNP model, we conducted single-subject and multisubject four-class classification experiments on two public datasets, MAHNOB-HCI and DEAP. Compared with existing feature extraction methods, the MESNP model significantly enhances the multiclass emotional classification performance in the single-subject and multisubject conditions. To evaluate the online version of the proposed MESNP model, we designed an online emotion monitoring system. We recruited 14 participants to conduct the online emotion decoding experiments. The average online experimental accuracy of the 14 participants was 84.56%, indicating that our model can be applied in affective brain-computer interface (aBCI) systems. The offline and online experimental results demonstrate that the proposed MESNP model effectively captures discriminative graph topology patterns and significantly improves emotion classification performance. Moreover, the proposed MESNP model provides a new scheme for extracting features from strongly coupled array signals.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Trans Neural Netw Learn Syst
dc.relation.isbasedon	10.1109/TNNLS.2023.3238519
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.
dc.type	Journal Article
utslib.citation.volume	PP
utslib.location.activity	United States
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
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-26T03:19:49Z
pubs.issue	99
pubs.publication-status	Published online
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and increase the stability of emotion recognition, we propose an effective emotion recognition model that performs multicategory emotion recognition with multiple emotion-related spatial network topology patterns (MESNPs) by learning discriminative graph topologies in EEG brain networks. To evaluate the performance of our proposed MESNP model, we conducted single-subject and multisubject four-class classification experiments on two public datasets, MAHNOB-HCI and DEAP. Compared with existing feature extraction methods, the MESNP model significantly enhances the multiclass emotional classification performance in the single-subject and multisubject conditions. To evaluate the online version of the proposed MESNP model, we designed an online emotion monitoring system. We recruited 14 participants to conduct the online emotion decoding experiments. The average online experimental accuracy of the 14 participants was 84.56%, indicating that our model can be applied in affective brain-computer interface (aBCI) systems. The offline and online experimental results demonstrate that the proposed MESNP model effectively captures discriminative graph topology patterns and significantly improves emotion classification performance. Moreover, the proposed MESNP model provides a new scheme for extracting features from strongly coupled array signals.

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http://hdl.handle.net/10453/177162