In general,a large amount of training data can effectively improve the classification performance of the Steady-State Visually Evoked Potential(SSVEP)-based Brain-Computer Interface(BCI)system.However,it will prolong the training time and considerably restrict the practicality of the system.This study proposed a SSVEP nonlinear signal model based on the Volterra filter,which could reconstruct stable reference signals using relatively small number of training targets by transfer learning,thereby reducing the training cost of SSVEP-BCI.Moreover,this study designed a transfer-extended Canonical Correlation Analysis(t-eCCA)method based on the model to achieve cross-target transfer.As a result,in a single-target SSVEP experiment with 16 stimulus frequencies,t-eCCA obtained an average accuracy of 86.96％±12.87％across 12 subjects using only half of the calibration time,which exhibited no significant difference from the representative training classification algorithms,namely,extended canonical correlation analysis(88.32％±13.97％)and task-related component analysis(88.92％±14.44％),and was significantly higher than that of the classic non-training algorithms,namely,Canonical Correlation Analysis(CCA)as well as filter-bank CCA.Results showed that the proposed cross-target transfer algorithm t-eCCA could fully utilize the information about the targets and its stimulus frequencies and effectively reduce the training time of SSVEP-BCI.