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Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs

Lin, Zhonglin, Zhang, Changshui, Wu, Wei, Gao, Xiaorong·2006·IEEE Transactions on Biomedical Engineering

Abstract

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 fast Fourier transform (FFT)-based spectrum estimation method.

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Research works printed but not linked: 21

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Metadata

Authors
Lin, Z., Zhang, C., Wu, W., & Gao, X.
Venue
IEEE Transactions on Biomedical Engineering
Published
2006-12
Volume
53
Issue
12
Pages
2610-2614
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
OpenAlex
W3149679044
Cited by
724
References
0
FWCI
1.54
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs. https://app.cochat.ai/papers/10.1109/tbme.2006.886577
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Lin, Z., Zhang, C., Wu, W., & Gao, X. (2006). Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2006.886577

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