Brain computer interface system based on monocular vision and motor imagery for UAV indoor space target searching

This paper puts forward a brain computer interface (BCI) system that integrates the monocular vision navigation and decision subsystems to realize the indoor three-dimensional (3D) space target searching with a low speed Unmanned Aerial Vehicle (UAV). The monocular vision navigation subsystem provides 3D space feasible flight directions for the decision subsystem. It utilizes the Scale Invariant Feature Transform (SIFT) algorithm and Brute-Force (BF) algorithm to extract the key points and match the extracted feature points. Obstacles and their positions are estimated by comparing the changes in the size of key points and the ratio of the size of the “convex points” area of the interest targets. The decision subsystem collects four kinds of motor imaginary (MI) tasks (left/right-hand, feet and tongue) electroencephalogram (EEG) signals from 15 electrodes firstly. Then, it applies two fifth-order Butterworth Band-Pass Filter (BPF) to preprocess the raw MI tasks EEG signals. Next, it adopts the Common Spatial Pattern (CSP) to spatially filter the preprocess EEG signals. To implement the quaternary classification,the decision subsystem utilizes the single convolutional layer Convolutional Neural Network (CNN) to realize the MI tasks EEG signals feature extraction, classification and final feasible flight direction decision finally. During this process, the spectrograms of the spatially filtered signals are given as the input to CNN. The actual indoor 3D space target searching experiment verifies that this BCI system has good adaptability and control stability.