  A brain-computer interface (BCI) is a system that allows a human operator to
use only mental commands in controlling end effectors that interact with the
world around them. Such a system consists of a measurement device to record the
human user's brain activity, which is then processed into commands that drive a
system end effector. BCIs involve either invasive measurements which allow for
high-complexity control but are generally infeasible, or noninvasive
measurements which offer lower quality signals but are more practical to use.
In general, BCI systems have not been developed that efficiently, robustly, and
scalably perform high-complexity control while retaining the practicality of
noninvasive measurements. Here we leverage recent results from feedback
information theory to fill this gap by modeling BCIs as a communications system
and deploying a human-implementable interaction algorithm for noninvasive
control of a high-complexity robot swarm. We construct a scalable dictionary of
robotic behaviors that can be searched simply and efficiently by a BCI user, as
we demonstrate through a large-scale user study testing the feasibility of our
interaction algorithm, a user test of the full BCI system on (virtual and real)
robot swarms, and simulations that verify our results against theoretical
models. Our results provide a proof of concept for how a large class of
high-complexity effectors (even beyond robotics) can be effectively controlled
by a BCI system with low-complexity and noisy inputs.
