Using SSVEP-BCI to Continuous Control a Quadcopter with 4-DOF Motions^.

Brain-computer interfaces (BCIs) allow for translating electroencephalogram (EEG) into control commands, e.g., to control a quadcopter. This study, we developed a practical BCI based on steady-state visually evoked potential (SSVEP) for continuous control of a quadcopter from the first-person perspective. Users watched with the video stream from a camera on the quadcopter. An innovative user interface was developed by embedding 12 SSVEP flickers into the video stream, which corresponded to the flight commands of 'take-off,' 'land,' 'hover,' 'keep-going,' 'clockwise,' 'counter-clockwise' and rectilinear motions in six directions, respectively. The command was updated every 400ms by decoding the collected EEG data using a combined classification algorithm based on task-related component analysis (TRCA) and linear discriminant analysis (LDA). The quadcopter flew in the 3-D space according to the control vector that was determined by the latest four commands. Three novices participated in this study. They were asked to control the quadcopter by either the brain or hands to fly through a circle and land on the target zone. As a result, the time consumption ratio of brain-control to hand-control was as low as 1.34, which means the BCI performance was close to hands. The information transfer rate reached a peak of 401.79 bits/min in the simulated online experiment. These results demonstrate the proposed SSVEP-BCI system is efficient for controlling the quadcopter.