  In this study, an advanced CCA-based algorithn called hybrid template
canonical correlation analysis (HTCCA) was proposed to improve the performance
of brain-computer interface (BCI) based on steady state visual evoked potential
(SSVEP) uuder data-linited condition. The HTCCA method combines the training
data from several subjects to construct SSVEP templates. The experinental
results evaluated on two public benchmark datasets showed that the proposed
method outperforms the compared methods in both detection accuracy and
information transfer rate when the number of tuials is small.Considering that
user-friendly experience will become a key factor for BCI in practical
application, it is very necessary to develop effective methods based on limited
EEG samples. This study demonstrates that the proposed method has great
potential in the application of SSVEP-based brain-computer interfaces.
