  Effective frequency recognition algorithms are critical in steady-state
visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In this
study, we present a hierarchical feature fusion framework which can be used to
design 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 and indicate that the extended CORRCA method used within the framework
significantly outperforms the original CORCCA method. Accordingly, the proposed
framework holds promise to enhance the performance of frequency recognition
methods in SSVEP-based BCIs.
