articleTop 10% cited

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

IEEE Transactions on Biomedical Engineering · 2007 · Vol. 54(6) · pp. 1172–1176

Zhonglin Lin✉(Tsinghua University)Changshui Zhang(Tsinghua University)Wei Wu(Tsinghua University)Xiaorong Gao(Tsinghua University)

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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.

EEG and Brain-Computer Interfaces Blind Source Separation Techniques Neuroscience and Neural EngineeringCanonical correlationBrain–computer interfaceElectroencephalographyFast Fourier transformNarrowbandSpeech recognitionComputer sciencePattern recognition (psychology)Artificial intelligenceCorrelation

MeSH terms

AlgorithmsElectroencephalographyEvoked Potentials, VisualFourier AnalysisImaginationPattern Recognition, AutomatedPhotic StimulationSensitivity and SpecificityStatistics as TopicUser-Computer InterfaceVisual CortexVisual PerceptionReproducibility of Results

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864

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