Brain-controlled selection of objects combined with autonomous robotic grasping

A Brain-Computer Interface (BCI) could help to restore mobility of severely paralyzed patients, for instance by prosthesis control. However, the currently achievable information transfer rate of noninvasive BCIs is insufficient to control complex prostheses continuously in many degrees of freedom. In this paper we present an autonomous system for grasping natural objects that compensates the low information flow from noninvasive BCIs. Using this system, one out of several objects can be grasped without any muscle activity. Rather, the grasp is initiated by decoded voluntary brain wave modulations. Object selection and grasping are performed in a virtual reality environment. A universal grasp planning algorithm calculates the trajectory of a gripper online. The system can be controlled after less than 10 min of training. We found that decoding accuracy increases over time and that an increased sense of agency achieved by permitting free selections renders the system to work most reliably.