Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.
basic_science · Level V
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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.
Medical subject headings
- Artificial Intelligence
- Cerebral Cortex
- Electroencephalography
- Evoked Potentials, Visual
- Pattern Recognition, Automated
- User-Computer Interface
- Visual Cortex