article
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
IEEE Transactions on Biomedical Engineering · 2006 · Vol. 53(12) · pp. 2610–2614
Zhonglin Lin✉(Tsinghua University)Changshui Zhang(Tsinghua University)Wei Wu(Tsinghua University)Xiaorong Gao(Tsinghua University)
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.
EEG and Brain-Computer InterfacesBlind Source Separation TechniquesNeural dynamics and brain functionCanonical correlationBrain–computer interfaceElectroencephalographyFast Fourier transformNarrowbandComputer scienceSpeech recognitionPattern recognition (psychology)Artificial intelligenceCorrelation
MeSH terms
AlgorithmsArtificial IntelligenceCerebral CortexElectroencephalographyEvoked Potentials, VisualHumansPattern Recognition, AutomatedStatistics as TopicUser-Computer InterfaceVisual Cortex
Citations
695
FWCI
1.40
field-weighted impact
References
24
Percentile
80%
vs. same field & year
Citations per year
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