Traditional Brain-Computer Interface (BCI) systems have many shortcomings, such as BCI based on motor imaginary requires a large number of exercises; BCI based on P300 requires multiple repetitions of flicker; the number of control commands of BCI based on SSVEP is affected by stimulation frequency and other factors. To this end, researchers have proposed a hybrid Brian-Computer Interface (hBCI). This paper mainly discusses the research progress of hBCI, and reviews three common types of hBCI, such as hBCI of multiple brain models, hBCI of various sensory stimuli, and hBCI of various signals. By analyzing the general principles, stimulation paradigms, experimental results, advantages and applications of the latest hBCI system, we find that using hBCI technology can improve the classification accuracy of BCI and increase the number of control commands, which is obviously better than single mode BCI.