This article proposes a novel shared intelligence system for brain-machine interface (BMI) teleoperated mobile robots where user's intention and robot's intelligence are concurrent elements equally participating in the decision process. We designed the system to rely on policies guiding the robot's behavior according to the current situation. We hypothesized that the fusion of these policies would lead to the identification of the next, most probable, location of the robot in accordance with the user's expectations. We asked 13 healthy subjects to evaluate the system during teleoperated navigation tasks in a crowded office environment with a keyboard (reliable interface) and with 2-class motor imagery (MI) BMI (uncertain control channel). Experimental results show that our shared intelligence system 1) allows users to efficiently teleoperate the robot in both control modalities; 2) it ensures a level of BMI navigation performances comparable to the keyboard control; 3) it actively assists BMI users in accomplishing the tasks. These results highlight the importance of investigating advanced human-machine interaction (HMI) strategies and introducing robotic intelligence to improve the performances of BMI actuated devices.