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Brain-Computer Interface for Physically Impaired People

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Brain-Computer Interface for Physically Impaired People American University in Cairo AUC Knowledge Fountain Theses and Dissertations 2-1-2016 Brain-computer interface for physically impaired people Nourhan Shamel El Sawi Follow this and additional works at: https://fount.aucegypt.edu/etds Recommended Citation APA Citation El Sawi, N. (2016).Brain-computer interface for physically impaired people [Master’s thesis, the American University in Cairo]. AUC Knowledge Fountain. https://fount.aucegypt.edu/etds/318 MLA Citation El Sawi, Nourhan Shamel. Brain-computer interface for physically impaired people. 2016. American University in Cairo, Master's thesis. AUC Knowledge Fountain. https://fount.aucegypt.edu/etds/318 This Thesis is brought to you for free and open access by AUC Knowledge Fountain. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AUC Knowledge Fountain. For more information, please contact [email protected]. The American University in Cairo School of Sciences and Engineering Brain-Computer Interface for Physically Impaired People By Nourhan Shamel El Sawi A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Robotics, Control and Smart Systems Under supervision of: Dr. Maki Habib Professor, Department of Mechanical Engineering August, 2016 DECLARATION I declare that this thesis is my work and that all used references have been cited and properly indicated. Nourhan Shamel DEDICATION I dedicate my thesis dissertation work to my family. A special feeling of gratitude to my loving parents Shamel El Sawi and Hanan Baligh for their continuous support, mom and dad you have always pushed me forward and believed in me. Also I would like to thank my supportive husband Asser Fahmy for providing me with all the financial support I needed to complete my thesis work. ACKNOWLEDGMENTS I would like to express my sincere gratitude to my advisor Dr. Maki Habib the director of Master program in Robotics, Control and Smart Systems (RCSS) in Mechanical Engineering Department, School of Sciences and Engineering at The American University in Cairo. Thanks for his continuous support of my Master’s study and research, for his patience, motivation, enthusiasm, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I also would like to thank all the students who voluntarily participated in my thesis tests for their time and enthusiasm to try new technology and for all their supportive words and impressions after trying the proposed system they really pushed me forward. ii ABSTRACT Brain-Computer interface (BCI) is a promising field of research that can change life as we know it, staring from the healthcare, home devices and armed force to video gaming. BCI provides a new communication channel between human and computers using brain signals to perform certain control actions. BCI systems can help physically impaired patients to increase their degree of independency, and give hope to ‘Locked-In Syndrome’ (LIS) Patients and Amyotrophic Lateral Sclerosis ALS to communicate again. The aim of this thesis is to develop a Generic BCI system that uses Blink and Wink as control signals. This system avoids problems like long training periods, the risks of flashing lights and using many electrodes and sophistication of hybrid systems. Blinks and Winks are signals generated by the eye lid muscles and it is considered as an artifact in the brain signals. In this study we propose the use of this artifact signals to recognize human intention. The reason behind choosing blink and wink signals is because its features can be distinguishable from the normal brain activities which allow the system to easily detect it. The Blink and Wink based BCI system uses oddball paradigm technique to facilitate the use for more interactive commands. The system is tested with P300 speller and smart home control panel. For P300 Speller test the system achieved 87.5% accuracy and for the smart home control panel test the system achieved 92.5% accuracy. The test shows that the developed system is generic and efficient as no offline training for calibration was done by any of the subjects while the system achieved better accuracy compared to other BCI systems. iii TABLE OF CONTENTS DECLARATION ................................................................................................................................................. iv DEDICATION ...................................................................................................................................................... v ACKNOWLEDGMENTS .................................................................................................................................... ii ABSTRACT ......................................................................................................................................................... iii TABLE OF CONTENTS .................................................................................................................................... iv LIST OF FIGURES ............................................................................................................................................ vii LIST OF TABLES ............................................................................................................................................... ix NOMENCLATURE ............................................................................................................................................. x Chapter 1 Introduction to Brain-Computer Interface .................................................................................. 1 1.1 Introduction ........................................................................................................................................... 1 1.2 Background ........................................................................................................................................... 2 1.2.1 What is Brain-Computer Interface (BCI)? ........................................................................................ 2 1.2.2 History of BCI .................................................................................................................................. 2 1.3 Motivation ............................................................................................................................................. 2 1.4 The Human Brain .................................................................................................................................. 3 1.4.1 Neuron structure ............................................................................................................................... 3 1.4.2 The structure of human brain ............................................................................................................ 5 1.5 Techniques for Measuring Brain Activity ............................................................................................. 6 1.5.1 Functional Magnetic Resonance Imaging (fMRI) ............................................................................ 7 1.5.2 Magnetoencephalography (MEG) .................................................................................................... 7 1.5.3 Electroencephalography (EEG) ........................................................................................................ 7 1.6 Electroencephalography (EEG) ............................................................................................................ 7 1.6.1 History of EEG ................................................................................................................................. 7 1.6.2 Introduction to EEG .......................................................................................................................... 8 1.6.3 The International 10-20 System ........................................................................................................ 8 1.6.4 EEG Rhythms of the Brain ............................................................................................................... 9 Chapter 2 Literature Review ......................................................................................................................... 14 2.1 BCI Systems......................................................................................................................................... 14 2.2 Architecture of BCI system .................................................................................................................. 14 2.2.1 EEG Signal Acquisition .................................................................................................................. 14 2.2.2 Signal Processing ............................................................................................................................ 14 2.2.3 Classification .................................................................................................................................. 15 2.2.4 Interface Commands ....................................................................................................................... 15 iv 2.3 Challenges with BCI Systems .............................................................................................................. 15 2.3.1 Information Transfer Rate (ITR) .................................................................................................... 15 2.3.2 Offline Training (Calibrating)
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