A Multiple Command UAV Control System Based on a Hybrid Brain-Computer Interface

Qingfu Wu, Shanghong Xie, Zhen Zeng, Qian Huang, Jiahui Pan

Abstract: The difficulty of UAV control in recent years lies in multidimensional movement in 3D space and improving control accuracy. To address this challenge, a UAV control method that incorporates a noninvasive hybrid brain-computer interface and gyroscope is proposed in this paper. We propose an efficient SSVEP deep learning network (CL-NET) based on one-dimensional convolution and a long short-term memory (LSTM) module that enables UAVs to move in the front, back, left and right directions. To improve the performance of CL-NET, an attention module to the network architecture is adopted. The takeoff and landing control of the UAV is realized by a blink state detector based on the electrooculogram (EOG) signal detection algorithm. The UAV was able to fly at a longitudinal tilt and rotate by detecting the current head posture with the help of a gyroscope. The proposed CL-NET model achieves an accuracy of 98.67% for the public dataset and an accuracy of 95.83% for the self-recorded dataset, which are both superior to the state-of-the-art models. In outdoor experiments involving six subjects, the proposed UAV control method reached an average information transfer rate of 44.64 bit/min. The efficiency and stability of our UAV control is thus demonstrated. With the proposed hybrid control strategy, our multidimensional UAV control can maintain excellent performance