title: 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 fast Fourier transform (FFT)-based spectrum estimation method.
authors.affiliation: Department of Automation, Tsinghua University, Room 3-120, FIT Building, Beijing 100084, China. linz102@mails.tsinghua.edu.cn
authors.forename: Zhonglin
authors.initials: Z
authors.lastname: Lin
authors.forename: Changshui
authors.initials: C
authors.lastname: Zhang
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002540:Cerebral Cortex / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D013223:Statistics as Topic; D014584:User-Computer Interface; D014793:Visual Cortex / Q000502:physiology*