Detection of steady-state visual-evoked potential using differential canonical correlation analysis

Steady-state visual evoked potential (SSVEP) is an electroencephalogram (EEG) activity elicited by periodic visual flickers. Frequency-coded SSVEP has been commonly adopted for functioning brain-computer interfaces (BCIs). Up to date, canonical correlation analysis (CCA), a multivariate statistical method, is considered to be state-of-the-art to robustly detect SSVEPs. However, the spectra of EEG signals often have a 1/f power-law distribution across frequencies, which inherently confines the CCA efficiency in discriminating between high-frequency SSVEPs and low-frequency background EEG activities. This study proposes a new SSVEP detection method, differential canonical correlation analysis (dCCA), by incorporating CCA with a notch-filtering procedure, to alleviate the frequency-dependent bias. The proposed dCCA approach significantly outperformed the standard CCA approach by around 6% in classifying SSVEPs at five frequencies (9-13Hz). This study could promote the development of high-performance SSVEP-based BCI systems.