  Recently, brain-computer interface (BCI) systems developed based on
steady-state visual evoked potential (SSVEP) have attracted much attention due
to their high information transfer rate (ITR) and increasing number of targets.
However, SSVEP-based methods can be improved in terms of their accuracy and
target detection time. We propose a new method based on canonical correlation
analysis (CCA) to integrate subject-specific models and subject-independent
information and enhance BCI performance. We propose to use training data of
other subjects to optimize hyperparameters for CCA-based model of a specific
subject. An ensemble version of the proposed method is also developed for a
fair comparison with ensemble task-related component analysis (TRCA). The
proposed method is compared with TRCA and extended CCA methods. A publicly
available, 35-subject SSVEP benchmark dataset is used for comparison studies
and performance is quantified by classification accuracy and ITR. The ITR of
the proposed method is higher than those of TRCA and extended CCA. The proposed
method outperforms extended CCA in all conditions and TRCA for time windows
greater than 0.3 s. The proposed method also outperforms TRCA when there are
limited training blocks and electrodes. This study illustrates that adding
subject-independent information to subject-specific models can improve
performance of SSVEP-based BCIs.
