Optimising the continuous control of brain-actuated robotic devices

Brain-machine interfaces (BMIs) are alternative communication channels that have allowed healthy and disabled people to control external devices from brain signals. In the last decades, the growing attention towards neurorobotics has led to the proliferation of several BMI-based systems for controlling different devices including telepresence robots, powered wheelchairs, robotic arms, and upper/lower-limb exoskeletons. Despite the potentialities of these systems, it has emerged the necessity to create new forms of interaction between the human and the robot in order to increase the granularity of the user's commands which are, in turn, translated into specific robot's actions. In this preliminary work, we present how artificial intelligence can be exploited to design and tune a model able to convert the user's intention into continuous robot's movements.