Brain Computer Interface system based on indoor semi-autonomous navigation and motor imagery for Unmanned Aerial Vehicle control

• BCI system is proposed to control the UAV continuously by motor imagery and semi-autonomous navigation system.
• The semi-autonomous navigation system is used to provide feasible directions and avoid obstacles.
• The improved cross correlation algorithm is used to extract the MI EEG features.
• The logistic regression algorithm is used to classify the MI EEG features.

This paper proposes a non-invasive Electroencephalogram (EEG)-based Brain Computer Interface (BCI) system to achieve the easy-to-use and stable control of a low speed Unmanned Aerial Vehicle (UAV) for indoor target searching. The BCI system for UAV control consists of two main subsystems responsible for decision and semi-autonomous navigation. The decision subsystem is established based on the analysis of motor imagery (MI) EEG. The improved cross-correlation method (CC) is used to accomplish the MI feature extraction and the logistic regression method (LR) is employed to complete the MI feature classification and decision. The average classification accuracy rate of the BCI system reaches to 94.36%. The semi-autonomous navigation subsystem is utilized to avoid obstacles automatically for UAV and provide feasible directions for decision subsystem. The actual indoor target searching experiment is carried out to verify the performance of this BCI system. The experiment validates the feasibility and effectiveness of this BCI system for low speed UAV control by using MI and semi-autonomous navigation.