Brain-Computer Interface (BCI) is a young research area for researchers. Increasing number of research activities improves several areas such as signal acquisition techniques, hardware development, machine learning, and signal processing and system integration. However, there are many disadvantages of conventional BCI approaches. For example, Motorimagery based BCIs requires extensive training of the subjects, P-300 based BCIs still requires several stimulus repetitions to obtain reliable accuracy and in SSVEP stimulus; number of commands is limited by the number of stimulus frequencies and many more. To overcome these disadvantages and further improvement in performance of the system, an increasing number of researchers have begun to explore hybrid BCI approaches, in which multiple BCI approaches are incorporated in a single BCI system. The purpose of this paper is to give a brief introduction to the different types of hybrid BCI techniques. There are many different types of hybrid BCI that can be used in a wide range of applications. The paradigm design plays a very important role in the performance of hybrid BCIs.

No abstract