An integration of BCI and vision system for Robotics applications: a review

With the rapid advancement of human–robot interaction technologies, the integration of electroencephalogram (EEG)–based brain–computer interface (BCI) systems with computer vision (CV) has become an emerging research trend in robotics. However, current studies mainly focus on either BCI-based or CV-based robotic control, while comprehensive reviews on their integration remain limited. This paper provides a systematic review of literature published since 2013 concerning the integration of BCI and CV technologies in robotics. The reviewed studies cover multiple application domains, including autonomous driving, robotic manipulators, and unmanned aerial vehicles. The review analyzes the algorithms and frameworks employed for multimodal fusion, shared control, and decision-making mechanisms. The contributions of this work are threefold: (1) it presents an organized overview of the research landscape on BCI–CV integration, helping new researchers to understand its conceptual foundation and development; (2) it highlights the evolution of fusion algorithms and their technical challenges; and (3) it discusses future research directions for intelligent shared-control systems combining neural and visual information. This study provides valuable insights into the design of next-generation human–robot interaction systems that achieve more natural and adaptive control through BCI–CV integration.