Hybrid brain-computer interface (hBCI) systems that combine steady-state visual evoked potential (SSVEP) and surface electromyography (sEMG) signals have attracted attention of researchers due to the advantage of exhibiting significantly improved system performance. However, almost all existing studies adopt low-frequency SSVEP to build hBCI. It produces much more visual fatigue than high-frequency SSVEP. Therefore, the current study attempts to build a hBCI based on high-frequency SSVEP and sEMG. With these two signals, this study designed and realized a 32-target hBCI speller system. Thirty-two targets were separated from the middle into two groups. Each side contained 16 sets of targets with different high-frequency visual stimuli (i.e., 31-34.75 Hz with an interval of 0.25 Hz). sEMG was utilized to choose the group and SSVEP was adopted to identify intra-group targets. The filter bank canonical correlation analysis (FBCCA) and the root mean square value (RMS) methods were used to identify signals. Therefore, the proposed system allowed users to operate it without system calibration. A total of 12 healthy subjects participated in online experiment, with an average accuracy of 93.52 ± 1.66% and the average information transfer rate (ITR) reached 93.50 ± 3.10 bits/min. Furthermore, 12 participants perfectly completed the free-spelling tasks. These results of the experiments indicated feasibility and practicality of the proposed hybrid BCI speller system.