Drone Movement Control by Electroencephalography Signals Based on BCI System

Abstract

Brain Computer Interface enables individuals to communicate with devices through ElectroEncephaloGraphy (EEG) signals in many applications that use brainwave-controlled units. This paper presents a new algorithm using EEG waves for controlling the movements of a drone by eye-blinking and attention level signals. Optimization of the signal recognition obtained is carried out by classifying the eyeblinking with a Support Vector Machine algorithm and converting it into 4-bit codes via an artificial neural network. Linear Regression Method is used to categorize the attention to either low or high level with a dynamic threshold, yielding a 1-bit code. The control of the motions in the algorithm is structured with two control layers. The first layer provides control with eye-blink signals, the second layer with both eye-blink and sensed attention levels. EEG signals are extracted and processed using a single channel NeuroSky MindWave 2 device. The proposed algorithm has been validated by experimental testing of five individuals of different ages. The results show its high performance compared to existing algorithms with an accuracy of 91.85 % for 9 control commands. With a capability of up to 16 commands and its high accuracy, the algorithm can be suitable for many applications.