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

Zhonglin Lin; Changshui Zhang; Wei Wu; Xiaorong Gao

doi:10.1109/tbme.2006.886577

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

Zhonglin Lin·Changshui Zhang·Wei Wu·Xiaorong Gao

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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 fast Fourier transform (FFT)-based spectrum estimation method.

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Algorithms Artificial Intelligence Cerebral Cortex Electroencephalography Evoked Potentials, Visual Humans Pattern Recognition, Automated Statistics as Topic User-Computer Interface Visual Cortex Journal Article Research Support, Non-U.S. Gov't

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Zhonglin Lin, Changshui Zhang, Wei Wu, Xiaorong Gao. 2006. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.. https://doi.org/10.1109/tbme.2006.886577

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