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

IEEE Trans Biomed Eng. 2006 Dec;53(12 Pt 2):2610-4. doi: 10.1109/tbme.2006.886577.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Artificial Intelligence*
  • Cerebral Cortex / physiology*
  • Electroencephalography / methods*
  • Evoked Potentials, Visual / physiology*
  • Humans
  • Pattern Recognition, Automated / methods*
  • Statistics as Topic
  • User-Computer Interface*
  • Visual Cortex / physiology*