Brain machine interfaces (BMI) have become important in systems neuroscience with the goal to restore motor function to paralyzed patients. I will assess the current ability of BMI devices to move objects. The topics discussed will include: (1) the bits of information generated by a BMI signal, (2) the limitations of including more neurons for generating a BMI signal, (3) the superiority of a BMI signal using single cells versus electroencephalography (EEG), (4) plasticity and BMI, (5) the selection of a neural code for generating BMI, (6) the suppression of body movements during BMI, and (7) the role of vision in BMI. I conclude that understanding fully how the brain generates movement is necessary before BMI can become a reasonable option for paralyzed patients. The implications of this conclusion are discussed. For more details see: Neuroscience, 2013, 255, 134-146.