The Brain Controlled Wheelchair (BCW) is a simple robotic system designed for people, such as locked-in people, who are not able to use physical interfaces like joysticks or buttons. Our goal is to develop a system usable in hospitals and homes with minimal infrastructure modifications, which can help these people regain some mobility. The main challenge is to provide continuous and precise 2D control of the wheelchair from a Brain Computer Interface, which is typically characterized by a a very low information transfer rate. Besides, as design constraints, we want our BCW to be safe, ergonomic and relatively low cost. The strategy we propose relies on 1) constraining the motion of the wheelchair along predefined guiding paths, and 2) a slow but accurate P300 EEG brain interface to select the destination in a menu. This strategy reduces control to the selection of the appropriate destination, thus requires little concentration effort from the user. Besides, the trajectory is predictable, which contributes to reduce stress, and eliminates frustration that may be associated with trajectories generated by an artificial agent. Two fast BCIs are proposed to allow stopping the wheelchair while in motion. A hybrid BCI was developed to combine the slow P300 BCI used for destination selection with a faster modality to stop the wheelchair while in motion. Experiments with healthy users were conducted to evaluate performances of the BCIs. We found that after a short calibration phase, the destination selection BCI allowed the choice of a destination within 15 seconds on average, with an error rate below 1%. The faster BCI used for stopping the wheelchair allowed a stop command to be issued within 5 seconds on average. Moreover, we investigated whether performance in the STOP interface would be affected during motion, and found no alteration relative to the static performance. Finally, the overall strategy was evaluated and compared to other brain controlled wheelchair projects. Despite the overhead required to select the destination on the interface, our wheelchair is faster than others (36% faster than MAIA): thanks to the motion guidance strategy, the wheelchair always follows the shortest path and a greater speed is possible. Comparison was also performed using a cost function that takes into account traveling time and concentration effort; our strategy yields by far the smallest cost (the best other score is 72% larger). This work resulted in a novel brain controlled wheelchair working prototype. It allows to navigate in a familiar indoor environment within a reasonable time. Emphasis was put on user's safety and comfort: the motion guidance strategy ensures smooth, safe and predictable navigation, while mental effort and fatigue are minimized by reducing control to destination selection.