Towards hierarchical BCIs for robotic control

There has been growing interest in brain-computer interfaces (BCIs) for controlling robotic devices and prosthetics directly using brain signals. Non-invasive BCIs, such as those based on electroencephalographic (EEG) signals, suffer from low signal-to-noise ratio, limiting the bandwidth of control. Invasive BCIs, on the other hand, allow fine-grained control but can leave users exhausted over long periods of time because of the amount of attention required for control on a moment-by-moment basis. In this paper, we address these problems using a new adaptive and hierarchical approach to brain-computer interfacing. The approach allows a user to teach the BCI system new skills on-the-fly; these learned skills are later invoked directly as high-level commands, relieving the user of tedious lower-level control. We demonstrate the approach using a hierarchical EEG-based BCI for controlling a humanoid robot. In a study involving four human subjects controlling the robot in a simulated home environment, each subject successfully used the BCI to teach the robot a new navigational task. They later were able to execute the same task by selecting the newly learned command from the BCI´s adaptive menu, avoiding the need for low-level control. A comparison of the performance of the system under low-level and hierarchical control revealed that hierarchical control is both faster and more accurate. Our results suggest that hierarchical BCIs can provide a flexible and robust way of controlling complex robotic devices, satisfying the dual goals of decreasing the cognitive load on the user while maintaining the ability to adapt to the user´s needs.