Common spatial pattern and linear discriminant analysis for motor imagery classification

Wu, SL; Wu, CW; Pal, NR; Chen, CY; Chen, SA; Lin, CT

Common spatial pattern and linear discriminant analysis for motor imagery classification

Wu, SL Wu, CW Pal, NR Chen, CY Chen, SA Lin, CT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, 2013, pp. 146 - 151
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Wu, SL	en_US
dc.contributor.author	Wu, CW	en_US
dc.contributor.author	Pal, NR	en_US
dc.contributor.author	Chen, CY	en_US
dc.contributor.author	Chen, SA	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, 2013, pp. 146 - 151	en_US
dc.identifier.isbn	9781467358712	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119834
dc.description.abstract	A Brain-Computer Interface (BCI) system provides a convenient way of communication for healthy subjects and subjects who suffer from severe diseases such as amyotrophic lateral sclerosis (ALS). Motor imagery (MI) is one of the popular ways of designing BCI systems. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify MI-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. We have also experimented with MI data generated in our lab. The proposed system is found to produce good results. In particular, using our EEG data for MI movements, we have obtained an average classification accuracy of 80% for two subjects using only 9 channels, without any feature selection. This proposed MI-based BCI system may be used in real life applications. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013	en_US
dc.relation.isbasedon	10.1109/CCMB.2013.6609178	en_US
dc.title	Common spatial pattern and linear discriminant analysis for motor imagery classification	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

A Brain-Computer Interface (BCI) system provides a convenient way of communication for healthy subjects and subjects who suffer from severe diseases such as amyotrophic lateral sclerosis (ALS). Motor imagery (MI) is one of the popular ways of designing BCI systems. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify MI-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. We have also experimented with MI data generated in our lab. The proposed system is found to produce good results. In particular, using our EEG data for MI movements, we have obtained an average classification accuracy of 80% for two subjects using only 9 channels, without any feature selection. This proposed MI-based BCI system may be used in real life applications. © 2013 IEEE.

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http://hdl.handle.net/10453/119834