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

2007/06/01 by Zhonglin Lin, Changshui Zhang, Wei Wu +1 · 30 citations
Neuroscience · Computer Science · #EEG and Brain-Computer Interfaces #Blind Source Separation Techniques #Neuroscience and Neural Engineering

paper · doi:10.1109/tbme.2006.889197

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.

Citations

• Relations Between Two Sets of Variates

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