الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Electroencephalography (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain [1].
A number of published research papers have indicated that there is enough individuality in the EEG recording, rendering it suitable as a tool for person authentication.
In recent years there has been a growing need for greater security for person authentication.
One of the potential solutions is to employ the innovative biometric authentication techniques. This research presents the research and development of a biometric authentication system based on electroencephalogram (EEG).
The goal of our work in this research is to investigate the possibility of person identification based on features extracted from person’s measured brain signals electrical activity (EEG) with a good rate of accuracy.
The work begins with a comprehensive literature review at which we investigate the different methods that have been applied for EEG based person identification in terms of preprocessing, feature extraction, and classification techniques.
After presenting a background about brain parts, EEG, methods and techniques used in our research, the work then continues with investigating the best combination of a set of features with one of the publically available EEG datasets and investigating different classification techniques. We then make our own dataset using Emotiv EPOC headset and investigate different classifiers.
Our main contribution is building the first dataset using EPOC and making it available for free for researchers in this field.