Brain-computer interface (BCI) technology aims to establish a new communication and control channel between the brain and the external environment that does not depend on peripheral nerves and muscles. The steadystate visual evoked potential (SSVEP) based braincomputer interface (BCI) is currently the noninvasive BCI paradigm with the highest information transmission rate, but it is still lower than the traditional interaction mode. In this paper, a hybrid braincomputer interface (BCI) combining surface electromyography (sEMG) and steadystate visual evoked potentials is proposed to further improve the information transmission rate of the system. A hybrid BCI system was realized by combining the SSVEP encoding at different frequencies with sEMG. The canonical correlation analysis method is used to identify the frequency of SSVEP signal, and the frequency domain analysis method is used to detect sEMG signal. Offline results from 8 healthy subjects show that the system can achieve an average accuracy of 84.28% and an average information transfer rate of 72.63 bits/min. These results lay the foundation for hybrid braincomputer interface studies combining surface EMG and steadystate visual evoked potentials.