This project was part of a undergraduate capstone course in Electrical Engineering (Robotics track). Our team decided to design and build a system that would allow a drone to be controlled via electroencephalography (EEG) from the brain. This was done by utilising steady-state visually envoked potentials (SSVEP), a recursive adaptive filter, and canonical correlation analysis to interpret the EEG signals and send them to the drone. We could then used LED’s that flashed at different frequencies to control the direction of the drone.

This was a fun project with a minimal amount of crashes! The programming was done in C and MATLAB.

We were also featured in an article at the then named Centre for Sensorimotor Neural Engineering.

Team members: Jon Lundlee, Sam Kinn, and Michael Schober