Before revision: Correlated component analysis (CORRCA) is a novel method for frequency recognition in steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI), which holds the potential to implement high-performance SSVEP-based BCI systems. CORRCA could provide multiple correlation coefficients to measure the correlation between two signals. However, only the largest one is selected as feature while ignoring all others in previous study, which loses the discriminating information. In current study, we proposed a weighting strategy with nonlinear function to combine all the correlation coefficients to enhance the performance of CORRCA method. Extensive experimental results on a benchmark dataset of thirty-five subjects  indicate that the proposed method significantly outperforms the original CORRCA method.
After revision: Correlated component analysis (CORRCA) is a novel method for frequency recognition in steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI), which holds the potential to implement high-performance frequency recognition methods. The proposed framework includes two primary technique for fusing features: spatial dimension fusion (SD) and frequency dimension fusion (FD). Both SD and FD fusions are obtained using a weighted strategy with a nonlinear function. To assess our novel methods, we used the correlated component analysis (CORRCA) method to investigate the efficiency and effectiveness of the proposed framework. Experimental results were obtained from a benchmark dataset of thirty-five subjects indicate that the proposed method significantly outperforms the original CORRCA method.