This paper proposes to utilize the phase information to enhance steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) based on a canonical correlation analysis neural network (CCA-NN). The preliminary offline results show that the proposed scheme can achieve a better classification accuracy than the standard CCA and the modified CCAs since it identifies the target by considering the flexible phase information.