  Integrating Brain-Machine Interfaces into non-clinical applications like
robot motion control remains difficult - despite remarkable advancements in
clinical settings. Specifically, EEG-based motor imagery systems are still
error-prone, posing safety risks when rigid robots operate near humans. This
work presents an alternative pathway towards safe and effective operation by
combining wearable EEG with physically embodied safety in soft robots. We
introduce and test a pipeline that allows a user to move a soft robot's end
effector in real time via brain waves that are measured by as few as three EEG
channels. A robust motor imagery algorithm interprets the user's intentions to
move the position of a virtual attractor to which the end effector is
attracted, thanks to a new Cartesian impedance controller. We specifically
focus here on planar soft robot-based architected metamaterials, which require
the development of a novel control architecture to deal with the peculiar
nonlinearities - e.g., non-affinity in control. We preliminarily but
quantitatively evaluate the approach on the task of setpoint regulation. We
observe that the user reaches the proximity of the setpoint in 66% of steps and
that for successful steps, the average response time is 21.5s. We also
demonstrate the execution of simple real-world tasks involving interaction with
the environment, which would be extremely hard to perform if it were not for
the robot's softness.
