Brain EEG time series selection: A novel graph-based approach for classification

Dai, C; Wu, J; Pi, D; Cui, L

Brain EEG time series selection: A novel graph-based approach for classification

Dai, C Wu, J Pi, D Cui, L

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Publication Type:: Conference Proceeding
Citation:: SIAM International Conference on Data Mining, SDM 2018, 2018, pp. 558 - 566
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Dai, C	en_US
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Pi, D	en_US
dc.contributor.author	Cui, L	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	SIAM International Conference on Data Mining, SDM 2018, 2018, pp. 558 - 566	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133341
dc.description.abstract	© 2018 by SIAM. Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs degrade the diagnosis performance and most previously developed methods ignore the necessity of EEG selection for classification. To this end, this paper proposes a novel maximum weight clique-based EEG selection approach, named mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques from an improved Fréchet distance-weighted undirected EEG graph simultaneously considering edge weights and vertex weights. Our mwcEEGs improves the classification performance by selecting intra-clique pairwise similar and inter-clique discriminative EEGs with similarity threshold δ. Experimental results demonstrate the algorithm effectiveness compared with the state-of the-art time series selection algorithms on real-world EEG datasets.	en_US
dc.relation.ispartof	SIAM International Conference on Data Mining, SDM 2018	en_US
dc.title	Brain EEG time series selection: A novel graph-based approach for classification	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 by SIAM. Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs degrade the diagnosis performance and most previously developed methods ignore the necessity of EEG selection for classification. To this end, this paper proposes a novel maximum weight clique-based EEG selection approach, named mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques from an improved Fréchet distance-weighted undirected EEG graph simultaneously considering edge weights and vertex weights. Our mwcEEGs improves the classification performance by selecting intra-clique pairwise similar and inter-clique discriminative EEGs with similarity threshold δ. Experimental results demonstrate the algorithm effectiveness compared with the state-of the-art time series selection algorithms on real-world EEG datasets.

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http://hdl.handle.net/10453/133341