Industrial human-robot collaboration (HRC) aims to combine the human intelligence and robotic capability to achieve the higher productiveness. In industrial HRC, the communication between human and robot is essential to enhance the understanding the intent of each other to make a more fluent collaboration. Brain-computer interface (BCI) is a technology that could record the user’s brain activity that can be translated into interaction messages (e.g., control commands) to the outside world, which is able to build a direct and efficient communication channel between human and robot. However, due to lacking of information feedback mechanism, it is challenging for BCI to control robots with high degree of freedom with the limited number of classifiable mental state. To address this problem, this paper proposes a close-loop BCI with contextual visual feedback by an augmented reality (AR) headset. In such BCI, the electroencephalogram (EEG) patterns from the multiple voluntary eye blinks are considered as the input and the its online detection algorithm is proposed whose average accuracy can reach 94.31%. Moreover, an AR-enable information feedback interface is designed which enable to achieve an interactive robotic path planning. A case study of an industrial HRC assembly task is also develop which shows that the proposed closed-up BCI could shorten the time of user input in human-robot interaction.