ABSTRACT
In the past decade, big progresses have been made in BCI studies. However, there have been a lot of challenges, including a low information transfer rate, multidimension/function control, man–machine adaptability, long-term robustness, and stability. In this chapter, we review the recent progress in hybrid brain–computer interface (BCIs; also called multimodal BCIs), which may provide potential solutions for addressing these challenges. In particular, four main classes of hybrid BCIs are introduced, including hybrid BCIs based on multibrain patterns, multisensory hybrid BCIs, hybrid BCIs based on multiple signals, and hybrid BCIs based on multiple intelligent techniques. We review state-of-the-art hybrid BCI systems by analyzing their general principles, paradigm, experimental results, advantages, and applications. We conclude that hybrid BCI techniques can be utilized to improve the target detection performance of BCIs and to perform multidimensional object control.
