BCI-DRONE CONTROL BASED ON THE CONCENTRATION LEVEL AND EYE BLINK SIGNALS USING A NEUROSKY HEADSET

Dr. Ali Abdulwahhab Mohammed; Ali H. Abdulwahhab; Dr. Alaa Hussein Abdulaal; Dr. Musaria Karim Mahmood; Dr. Indrit Myderrizi; Riyam Ali Yassin; Taha Talib Abdulridha; Dr. Morteza Valizadeh

doi:10.30572/2018/KJE/160339

Authors

Dr. Ali Abdulwahhab Mohammed Department of Remote Sensing, Al-Karkh University of Science
Ali H. Abdulwahhab Department of Electrical and Computer Engineering, Altinbas University
Dr. Alaa Hussein Abdulaal Department of Electrical Engineering, College of Engineering, Al-Iraqia University
Dr. Musaria Karim Mahmood Department of Energy Systems Engineering, Ankara Yildirim Beyazit University
Dr. Indrit Myderrizi Department of Electrical and Electronics Engineering, Istinye University
Riyam Ali Yassin Department of Electrical Engineering, Urmia University
Taha Talib Abdulridha College of Engineering, Al-Iraqia University
Dr. Morteza Valizadeh Department of Electrical Engineering, Urmia University

DOI:

https://doi.org/10.30572/2018/KJE/160339

Keywords:

Electroencephalogram, BCI system, Neurosky, Attention level, Eye blink

Abstract

Brain neurons activate Human movements by producing electrical bio-signals. Neuron activity is used in several technologies by operating their applications based on mind waves. The Brain-Computer Interface (BCI) technology enables a processor to connect with the brain using a signal received from the brain. This study proposes a drone controlled using EEG signals acquired by a Neurosky device based on the BCI system. Two active signals are adapted for controlling the drone motions: concentration brain signals portrayed by attention level and the eye blinks as an integer value. A dynamic classification method is implemented via a Linear Regression algorithm for attention-level code. The eye blinking generates a binary code to control the drone's motions. The accuracy of this code is improved through Artificial Neural Networks and Machine Learning techniques. These codes (attention level and eye blink codes) drive two controlling layers and manipulate nine possible drone movements. The experiment was evaluated with several users and showed high performance for the classification methods and developed algorithm. The experiment shows a 90.37% accuracy control that outperforms most existing experiments. Also, the experiment can support 16 commands, making the algorithm appropriate for various applications.

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