Frequency Recognition of Steady–State Visually Evoked Potentials Using Binary Subband Canonical Correlation Analysis with Reduced Dimension of Reference Signals

Abstract

This paper presents a frequency recognition method of steady-state visual evoked potentials (SSVEPs) using binary subbands with canonical correlation analysis (CCA). The first subband contains all the target frequencies of SSVEPs. The second one includes the SSVEP signal corresponding to a desired number of higher order stimulus frequencies. This one is obtained by filtering out of re- quired range of lower order stimuli. The full dimension of artificial reference signals are used with first subband, whereas a reduced dimension of references is employed with second subband to compute canonical correlation. The weighted sum of the obtained correlation values are used to recognize the frequency of an SSVEP. The experimental results show the superiority of the proposed method compared to the state-of-the-art recognition methods.