Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs

Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on t...

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Bibliographic Details
Published inIEEE transactions on biomedical engineering Vol. 54; no. 6; pp. 1172 - 1176
Main Authors Lin, Zhonglin, Zhang, Changshui, Wu, Wei, Gao, Xiaorong
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2007
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Automation
Brain computer interface
Brain computer interfaces
canonical correlation analysis
Discrete Fourier transforms
electroencephalogram
Electroencephalography
Frequency
Information analysis
Signal analysis
Signal to noise ratio
Spectral analysis
Steady-state
steady-state visual evoked potentials
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Summary:Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used FFT (fast Fourier transform)-based spectrum estimation method
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ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2006.889197