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Phase Information Enhanced SSVEP-BCI Using a Canonical Correlation Analysis Neural Network
C. M. Wong; F. Wan; K. F. Lao; M. I. Vai
2013
Conference Namethe 5th International Brain-Computer Interface (BCI) Meeting
Source Publicationthe Proceedings of the 5th International Brain-Computer Interface (BCI) Meeting
Conference DateJune 3-7, 2013
Conference PlaceCA, USA
Abstract

This paper proposes to utilize the phase information to enhance steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) based on a canonical correlation analysis neural network (CCA-NN). The preliminary offline results show that the proposed scheme can achieve a better classification accuracy than the standard CCA and the modified CCAs since it identifies the target by considering the flexible phase information.

DOIhttp://dx.doi.org/10.3217/978-3-85125-260-6-155
Language英语
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Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
AffiliationUniversity of Macau, Macao
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
C. M. Wong,F. Wan,K. F. Lao,et al. Phase Information Enhanced SSVEP-BCI Using a Canonical Correlation Analysis Neural Network[C],2013.
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