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

• Published in: Proceedings of ... International Joint Conference on Neural Networks pp. 1 - 8
• Main Authors: Wu, Qingfu, Xie, Shanghong, Zeng, Zhen, Huang, Qian, Pan, Jiahui
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.06.2023
• Subjects: Autonomous aerial vehicles; Brain modeling; Brain-computer interfaces; Control systems; Electrooculography; Productivity; Three-dimensional displays
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN54540.2023.10191621

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