CenEEGs: Valid EEG selection for classification

Dai, C; Pi, D; Becker, SI; Wu, J; Cui, L; Johnson, B

CenEEGs: Valid EEG selection for classification

Dai, C Pi, D Becker, SI Wu, J Cui, L Johnson, B

Permalink

Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Knowledge Discovery from Data, 2020, 14, (2), pp. 1-25
Issue Date:: 2020-02-18

Closed Access

	Filename	Description	Size
	3371153.pdf	Published version	9.03 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Dai, C
dc.contributor.author	Pi, D
dc.contributor.author	Becker, SI
dc.contributor.author	Wu, J
dc.contributor.author	Cui, L
dc.contributor.author	Johnson, B
dc.date.accessioned	2020-11-07T20:35:52Z
dc.date.available	2020-11-07T20:35:52Z
dc.date.issued	2020-02-18
dc.identifier.citation	ACM Transactions on Knowledge Discovery from Data, 2020, 14, (2), pp. 1-25
dc.identifier.issn	1556-4681
dc.identifier.issn	1556-472X
dc.identifier.uri	http://hdl.handle.net/10453/143830
dc.description.abstract	© 2020 Association for Computing Machinery. This article explores valid brain electroencephalography (EEG) selection for EEG classification with different classifiers, which has been rarely addressed in previous studies and is mostly ignored by existing EEG processing methods and applications. Importantly, traditional selection methods are not able to select valid EEG signals for different classifiers. This article focuses on a source control-based valid EEG selection to reduce the impact of invalid EEG signals and aims to improve EEG-based classification performance for different classifiers. We propose a novel centroid-based EEG selection approach named CenEEGs, which uses a scale-and-shift-invariance similarity metric to measure similarities of EEG signals and then applies a globally optimal centroid strategy to select valid EEG signals with respect to a similarity threshold. A detailed comparison with several state-of-the-art time series selection methods by using standard criteria on 8 EEG datasets demonstrates the efficacy and superiority of CenEEGs for different classifiers.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM Transactions on Knowledge Discovery from Data
dc.relation.isbasedon	10.1145/3371153
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	CenEEGs: Valid EEG selection for classification
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-07T20:35:41Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	2

Abstract:

© 2020 Association for Computing Machinery. This article explores valid brain electroencephalography (EEG) selection for EEG classification with different classifiers, which has been rarely addressed in previous studies and is mostly ignored by existing EEG processing methods and applications. Importantly, traditional selection methods are not able to select valid EEG signals for different classifiers. This article focuses on a source control-based valid EEG selection to reduce the impact of invalid EEG signals and aims to improve EEG-based classification performance for different classifiers. We propose a novel centroid-based EEG selection approach named CenEEGs, which uses a scale-and-shift-invariance similarity metric to measure similarities of EEG signals and then applies a globally optimal centroid strategy to select valid EEG signals with respect to a similarity threshold. A detailed comparison with several state-of-the-art time series selection methods by using standard criteria on 8 EEG datasets demonstrates the efficacy and superiority of CenEEGs for different classifiers.

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

http://hdl.handle.net/10453/143830