Fusing Canonical Coefficients for Frequency Recognition in SSVEP-Based BCI

The canonical correlation analysis (CCA)-based frequency recognition method is one of the widely used methods in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI), and several extended version were proposed in the past decade. However, these methods only adopt the maximal correlation coefficient provided by the CCA and discard other coefficients. This operation may lead to the loss of discriminative information that exists in the discarded coefficients. In the current study, we proposed to fuse all the correlation coefficients of the CCA with a nonlinear weighting function when performing frequency recognition with CCA method, termed as FoCCA. Evaluated on a benchmark dataset of thirty-five subjects, the experimental results demonstrated that the classification accuracy and information transfer rate (ITR) of FoCCA are significantly higher than those of the standard CCA at various time windows. Fusing correlation coefficients could be a new strategy to improve the performance of other extended CCA methods, which holds the potential to implement high-performance SSVEP-based BCI systems in the future.