Title :
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
Author :
Zhonglin Lin ; Changshui Zhang ; Wei Wu ; Xiaorong Gao
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing
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
Keywords :
correlation methods; electroencephalography; feature extraction; handicapped aids; medical signal processing; visual evoked potentials; EEG; SSVEP-based BCI; brain computer interface; canonical correlation analysis; electroencephalogram; frequency feature extraction; frequency recognition; narrowband frequency component extraction; steady-state visual evoked potentials; Automation; Brain computer interfaces; Discrete Fourier transforms; Electroencephalography; Fast Fourier transforms; Frequency; Information analysis; Signal analysis; Spectral analysis; Steady-state; Brain computer interface; canonical correlation analysis; electroencephalogram; steady-state visual evoked potentials; Algorithms; Electroencephalography; Evoked Potentials, Visual; Fourier Analysis; Imagination; Pattern Recognition, Automated; Photic Stimulation; Reproducibility of Results; Sensitivity and Specificity; Statistics as Topic; User-Computer Interface; Visual Cortex; Visual Perception;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2006.886577
