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Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs
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 FFT (fast Fourier transform)-based spectrum estimation method.
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Metadata
- Authors
- Lin, Z., Zhang, C., Wu, W., & Gao, X.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2007-06
- Volume
- 54
- Issue
- 6
- Pages
- 1172-1176
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2098725211
- Cited by
- 867
- References
- 0
- FWCI
- 5.57
- Type
- journal article
- Language
- en
- Open access
- closed
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