A novel classification method for motor imagery based on Brain-Computer Interface

Chen, CY; Wu, CW; Lin, CT; Chen, SA

A novel classification method for motor imagery based on Brain-Computer Interface

Chen, CY Wu, CW Lin, CT Chen, SA

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 4099-4102
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Chen, CY
dc.contributor.author	Wu, CW
dc.contributor.author	Lin, CT
dc.contributor.author	Chen, SA
dc.date	2014-07-06
dc.date.accessioned	2021-04-21T03:16:51Z
dc.date.available	2021-04-21T03:16:51Z
dc.date.issued	2014-01-01
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 4099-4102
dc.identifier.isbn	9781479914845
dc.identifier.issn	2161-4393
dc.identifier.uri	http://hdl.handle.net/10453/148227
dc.description.abstract	Brain computer interface (BCI) is known as a good way to communicate between brain and computer or other device. There are many kinds of physiological signal can operate BCI systems. Motor imagery (MI) has been demonstrated to be a good way to operate a BCI system. In some recent studies about MI based BCI systems, low accuracy rate and time consuming are common problems. In this thesis, a novel motor imagery algorithm is proposed to improve the accuracy rate and computational efficiency at the same time. 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. This novel algorithm with high accuracy rate and efficiency can be applied to real time BCI system in real-life applications.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks
dc.relation.ispartof	International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartofseries	IEEE International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/IJCNN.2014.6889535
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A novel classification method for motor imagery based on Brain-Computer Interface
dc.type	Conference Proceeding
utslib.location.activity	Beijing, PEOPLES R CHINA
utslib.for	0902 Automotive 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/Strength - AAII - Australian Artificial Intelligence Institute
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	2021-04-21T03:16:49Z
pubs.finish-date	2014-07-11
pubs.publication-status	Published
pubs.start-date	2014-07-06

Abstract:

Brain computer interface (BCI) is known as a good way to communicate between brain and computer or other device. There are many kinds of physiological signal can operate BCI systems. Motor imagery (MI) has been demonstrated to be a good way to operate a BCI system. In some recent studies about MI based BCI systems, low accuracy rate and time consuming are common problems. In this thesis, a novel motor imagery algorithm is proposed to improve the accuracy rate and computational efficiency at the same time. 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. This novel algorithm with high accuracy rate and efficiency can be applied to real time BCI system in real-life applications.

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http://hdl.handle.net/10453/148227