@article{canal.21b,
    author = 	 {Canal, G. and Diaz-Mercado, Y. and Egerstedt, M. and Rozell, C.},
    title = 	 {A low-complexity brain-computer interface for high-complexity robot swarm control},
	journal = {IEEE in Transactions on Neural Systems \& Rehabilitation Engineering},
    year =	 2023,
	month = mar,
	volume = 31,
	pages = 1816--1825,
	abstract = {Brain-computer interfaces (BCI) are systems that consist of hardware to measure a human user's brain activity, an interaction algorithm to map the user's mental commands to control signals, and an end effector that the user operates via these control signals. This direct link between brain and effector provides a means for paralyzed users to circumvent muscular pathways and interact with everyday devices as well as an augmented interface for healthy users. Although BCIs with invasive neural measurements have had experimental success in controlling high-complexity effectors (e.g., robotic arms) with many degrees of freedom, such BCIs are only available in research settings and require a surgical procedure for electrode implantation. BCIs with noninvasive measurements (e.g., scalp electrode recordings via an electroencephalogram (EEG)) are more widely implementable due to their relative ease of use and lower cost, but are limited to controlling comparatively simpler effectors (e.g., basic wheelchair control, cursor control) with few degrees of freedom due to lower signal-to-noise ratios. 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.},
	doi = {https://doi.org/10.1109/TNSRE.2023.3257261},
	url = {https://arxiv.org/abs/2205.14265}
  }