Frequency detection for SSVEP-based BCI using deep canonical correlation analysis

Canonical correlation analysis (CCA) has been successfully used for extracting frequency components of steady-state visual evoked potential (SSVEP) in electroencephalography (EEG). Recently, a few efforts on CCA-based SSVEP methods have been made to demonstrate the benefits for brain computer interface (BCI). Most of these methods are limited to linear CCA. In this paper consider a deep extension of CCA where input data are processed through multiple layers before their correlations are computed. To our best knowledge, it is the first time to apply deep CCA (DCCA) to the task of frequency component extraction in SSVEP. Our empirical study demonstrates that DCCA extracts more robust feature, which has significantly higher signal to noise ratio (SNR) compared to those of CCA, and it results in better performance in classification with the averaged accuracy of 92%.