Developing a novel multi-fusion brain-computer interface (BCI) system with particle swarm optimization for motor imagery task

Hsieh, TY; Lin, YY; Liu, YT; Fang, CN; Lin, CT

Developing a novel multi-fusion brain-computer interface (BCI) system with particle swarm optimization for motor imagery task

Hsieh, TY Lin, YY Liu, YT Fang, CN

Lin, CT

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2015, 2015-November
Issue Date:: 2015-11-25

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Field	Value	Language
dc.contributor.author	Hsieh, TY	en_US
dc.contributor.author	Lin, YY	en_US
dc.contributor.author	Liu, YT	en_US
dc.contributor.author	Fang, CN https://orcid.org/0000-0002-7889-4564	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2015-11-25	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2015, 2015-November	en_US
dc.identifier.isbn	9781467374286	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121268
dc.description.abstract	© 2015 IEEE. In this paper, we develop a novel multi-fusion brain-computer interface (BCI) based on linear discriminant analysis (LDA) to deal with motor imagery (MI) classification problem. We combine filter bank and sub-band common spatial pattern (SBCSP) to extract features from EEG data in the preprocessing phase, and then LDA classifiers are applied to classify brain activities to identify either left or right hand imagery. To further foster the performance of the proposed system, a fuzzy integral (FI) approach is employed to fuse information sources, and particle swarm optimization (PSO) algorithm is exploited to globally update parameters in the fusion structure. Consequently, our experimental results indicate that the proposed system provides superior performance compared to other approaches.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2015.7337842	en_US
dc.title	Developing a novel multi-fusion brain-computer interface (BCI) system with particle swarm optimization for motor imagery task	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015-November	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2015-November	en_US

Abstract:

© 2015 IEEE. In this paper, we develop a novel multi-fusion brain-computer interface (BCI) based on linear discriminant analysis (LDA) to deal with motor imagery (MI) classification problem. We combine filter bank and sub-band common spatial pattern (SBCSP) to extract features from EEG data in the preprocessing phase, and then LDA classifiers are applied to classify brain activities to identify either left or right hand imagery. To further foster the performance of the proposed system, a fuzzy integral (FI) approach is employed to fuse information sources, and particle swarm optimization (PSO) algorithm is exploited to globally update parameters in the fusion structure. Consequently, our experimental results indicate that the proposed system provides superior performance compared to other approaches.

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http://hdl.handle.net/10453/121268