Implementation of a New Algorithm for Drone Control Using BCI System

The brain neurons are responsible of activating the human movement, and generating the electrical bio-signal inside the brain. These neurons features are invested in several technologies, which are used the mind waves in controlling the applications. Brain Computer Interface (BCI) is an interfacing technology between the mind and a processor by sensing brain signal and employing it to perform different tasks. The BCI device used in this research is the one-channel NeuroSky mindwave 2. This paper presents an EEG waves-based method to control a device “here a drone” using two active signals; the eye blinking and the concentration level described by attention signal. The dynamic classification of these signals is performed via Support Vector Machine algorithm and Linear Regression algorithm for attention. Quantified signals are then used to generate a binary code used for drone control. Binary code is used as input for a control algorithm based on two-layer control to manipulate a drone with 9 possible movements. Experiments are conducted with various individuals where the results show its high performance of the developed algorithm and the signal classification methods. The system outperforms most of the existing systems with an accuracy of 90.37%. Moreover, the algorithms offer a capability of performing 16 commands making it suitable for various applications. Keywords: Brain Computer Interface, EEG signals, NeuroSky device, attention level, eye-blink. Introduction There are billions of neurons inside the human mind responsible of human movements, thoughts, emotions and behaviours. These neurons features are invested in several technology, which are used the mind activities to implement different functions [1]. Usually, remote control is used to perform the tasks and application control. Since the work of Hans Berger on mind activities in [2], the researchers work on developing a system to exploit mind internal signals. The Brain Computer Interface (BCI) system is a communication technology between the brain and the computer aiming to create a connection channel for sending and managing the signals from the human mind to the hardware of the system [3]. BCI system adopts two brain signal sensing methods which are; the invasive and non-invasive methods [4]. The invasive method demands surgical involvement for implanting the electrodes in the cerebral cortex to acquire the brain signals providing high quality signals and good SNR [5]. The non-invasive method requires installing the electrodes in a mindset device according to the standardized 10-20 electrodes map without any surgery [6]. By using the right tools, every action generates by the brain can be used as input data to control various applications in medicine, gaming, among others, prompting the increased interest on BCI developing [7]. Recently, controlling a drone, wheelchair for persons with disabilities or BCI-based robot is among the most interesting topics as shown in Fig 1 [8].
via Support Vector Machine algorithm and Linear Regression algorithm for attention. Quantified signals are then used to generate a binary code used for drone control. Binary code is used as input for a control algorithm based on two-layer control to manipulate a drone with 9 possible movements. Experiments are conducted with various individuals where the results show its high performance of the developed algorithm and the signal classification methods. The system outperforms most of the existing systems with an accuracy of 90.37%. Moreover, the algorithms offer a capability of performing 16 commands making it suitable for various applications.