Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.
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.
Lin
Zhonglin
Z
Department of Automation, Tsinghua University, Beijing 100084, China. linz102@mails.tsinghua.edu.cn
Zhang
Changshui
C
Wu
Wei
W
Gao
Xiaorong
X
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005583:Fourier Analysis; D007092:Imagination / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010775:Photic Stimulation / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D014584:User-Computer Interface; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*