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Canonical correlation analysis neural network for steady-state visual evoked potentials based brain-computer interfaces
Lao K.F.; Wong C.M.; Wan F.; Mak P.I.; Mak P.U.; Vai M.I.
2013-08-01
Conference Name10th International Symposium on Neural Networks (ISNN 2013)
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7952 LNCS
IssuePART 2
Pages276-283
Conference DateJuly 4-6, 2013
Conference PlaceDalian, China
Abstract

Canonical correlation analysis (CCA) is a promising feature extraction technique of steady state visual evoked potential (SSVEP)-based brain computer interface (BCI). Many researches have showed that CCA performs significantly better than the traditional methods. In this paper, the neural network implementation of CCA is used for the frequency detection and classification in SSVEP-based BCI. Results showed that the neural network implementation of CCA can achieve higher classification accuracy than the method of power spectral density analysis (PSDA), minimum energy combination (MEC) and similar performance to the standard CCA method. © 2013 Springer-Verlag Berlin Heidelberg.

KeywordBrain Computer Interface (Bci) Canonical Correlation Analysis (Cca) Neural Network Steady-state Visual Evoked Potential (Ssvep)
DOI10.1007/978-3-642-39068-5-34
URLView the original
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Citation statistics
Document TypeConference paper
CollectionDEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Lao K.F.,Wong C.M.,Wan F.,et al. Canonical correlation analysis neural network for steady-state visual evoked potentials based brain-computer interfaces[C],2013:276-283.
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