Towards hierarchical BCIs for robotic control

摘要

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.