Using a brain-machine interface (BMI), a non-human primate (NHP) was trained to control a mobile robotic platform in real time using spike activity from the motor cortex, enabling self-motion through brain-control. The decoding model was initially trained using neural signals recorded when the NHP controlled the platform using a joystick. Using this decoding model, we compared the performance of the BMI during brain control with and without the use of a dummy joystick, and found that the success ratio dropped by 40% and time taken increased by 45% when the dummy joystick was removed. Performance during full brain control was only restored after a recalibration of the decoding model. We aimed to understand the differences in the underlying neural representations of movement intentions with and without the use of a dummy joystick, and showed that there were significant changes in both directional tuning, as well as global firing rates. These results indicate that the strategies used by the NHP for self-motion were different depending on whether a dummy joystick was present. We propose that a recalibration of the decoding model is an important step during the implementation of a BMI system for self-motion.