A Robot Control Platform for Motor Impaired People

Brain-machine interfaces (BMI) open new opportunities to control robotic devices as they provide the feasibility to translate brain signals into commands. Severely motor impaired people who have lost muscle control could benefit from this technique to control assistive devices, which support them in daily life. However, non-invasive BMIs can distinguish only a few different commands with relatively high error rates, which makes the asynchronous control of a robot with multiple degrees of freedom challenging. Here, we introduce a novel robotic grasping system, which combines scene recognition techniques and autonomous path planning with user interaction instantiated by a hybrid control system based on the electroencephalogram and the electrooculogram. The results show that healthy subjects can reliably perform a grasp-and-place task, arranging four objects at defined positions within 133-331s (193.6 ±61.5s), while they require only a few corrections. Our robot control platform proved to work solely with electrophysiological control signals and thus, constitutes a basis to perform various robot actions initiated by motor-impaired people.