Multi-channel EEG signals classification via CNN and multi-head self-attention on evidence theory

Zhang, L; Xiao, F; Cao, Z

Multi-channel EEG signals classification via CNN and multi-head self-attention on evidence theory

Zhang, L Xiao, F Cao, Z

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Information Sciences, 2023, 642
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Zhang, L
dc.contributor.author	Xiao, F
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.date.accessioned	2024-03-26T03:24:00Z
dc.date.available	2024-03-26T03:24:00Z
dc.date.issued	2023-09-01
dc.identifier.citation	Information Sciences, 2023, 642
dc.identifier.issn	0020-0255
dc.identifier.issn	1872-6291
dc.identifier.uri	http://hdl.handle.net/10453/177164
dc.description.abstract	Electroencephalography (EEG) provides valuable physiological information to identify human activities. However, it can be difficult to analyze the EEG data in human patterns identification, because both subjective and objective factors can easily affect sensitivity. In this study, a novel multi-head self-attention convolutional neural networks (CNN) framework based on Dempster-Shafer (D-S) evidence theory, called ETNN, is proposed to classify the EEG signal. The ETNN model considers the multi-type networks and fuses multi-output with D-S evidence theory, which can handle the EEG data more reasonably. In particular, a classification algorithm for EEG signals is derived with information fusion. Finally, an application for event-related potential signal classification and sensitivity analysis is used to demonstrate the effectiveness of the proposed ETNN model compared with existing classification techniques.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2023.119107
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Multi-channel EEG signals classification via CNN and multi-head self-attention on evidence theory
dc.type	Journal Article
utslib.citation.volume	642
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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	2024-03-26T03:23:59Z
pubs.publication-status	Published
pubs.volume	642

Abstract:

Electroencephalography (EEG) provides valuable physiological information to identify human activities. However, it can be difficult to analyze the EEG data in human patterns identification, because both subjective and objective factors can easily affect sensitivity. In this study, a novel multi-head self-attention convolutional neural networks (CNN) framework based on Dempster-Shafer (D-S) evidence theory, called ETNN, is proposed to classify the EEG signal. The ETNN model considers the multi-type networks and fuses multi-output with D-S evidence theory, which can handle the EEG data more reasonably. In particular, a classification algorithm for EEG signals is derived with information fusion. Finally, an application for event-related potential signal classification and sensitivity analysis is used to demonstrate the effectiveness of the proposed ETNN model compared with existing classification techniques.

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http://hdl.handle.net/10453/177164