While SSVEP-BCI has been widely developed to control external devices, most of them rely on the discrete control strategy. The continuous SSVEP-BCI enables users to continuously deliver commands and receive real-time feedback from the devices, but it suffers from the transition state problem, a period the erroneous recognition, when users shift their gazes between targets. To resolve this issue, we proposed a novel calibration-free Bayesian approach by hybridizing SSVEP and electrooculography (EOG). First, canonical correlation analysis (CCA) was applied to detect the evoked SSVEPs, and saccade during the gaze shift was detected by EOG data using an adaptive threshold method. Then, the new target after the gaze shift was recognized based on a Bayesian optimization approach, which combined the detection of SSVEP and saccade together and calculated the optimized probability distribution of the targets. Eighteen healthy subjects participated in the offline and online experiments. The offline experiments showed that the proposed hybrid BCI had significantly higher overall continuous accuracy and shorter gaze-shifting time compared to FBCCA, CCA, MEC, and PSDA. In online experiments, the proposed hybrid BCI significantly outperformed CCA-based SSVEP-BCI in terms of continuous accuracy (77.61 ± 1.36%vs. 68.86 ± 1.08% and gaze-shifting time (0.93 ± 0.06s vs. 1.94 ± 0.08s). Additionally, participants also perceived a significant improvement over the CCA-based SSVEP-BCI when the newly proposed decoding approach was used. These results validated the efficacy of the proposed hybrid Bayesian approach for the BCI continuous control without any calibration. This study provides an effective framework for combining SSVEP and EOG, and promotes the potential applications of plug-and-play BCIs in continuous control.