In the case of brain-controlled robots and wheelchairs, Millán's group has lead the development of a shared autonomy approach in the framework of the European MAIA project that solves the two problems mentioned above. This approach estimates the user's mental intent asynchronously and provides appropriate assistance for navigation of the wheelchair. This approach has shown to drastically improve BCI driving performance (Vanacker et al., 2007; Galán et al., 2008; Millán et al., 2009; Tonin et al., 2010). Despite that asynchronous spontaneous BCIs seem to be the most natural and suitable alternative, there are a few examples of evoked BCIs for the control of wheelchairs (Rebsamen et al., 2007; Iturrate et al., 2009). Both systems are based on P300, a potential evoked by an awaited infrequent stimulus. To evoke the P300, the system flashes the possible predefined target destinations several times in a random order. The subject's choice is the stimulus that elicits the largest P300. Then, the intelligent wheelchair reaches the selected target autonomously. Once there, it stops and the subject can select another destination – a process that takes around 10 s. A similar P300 approach has been followed to control a humanoid robot (Bell et al., 2008).