Ahmed Abdelhamid Mohamed Torki <br/><br/>
Automatic detection of driver drowsiness during simulated driving using brainwaves changes as indicator /  
 الكشف الاوتوماتيكي لنعاس السائق اثناء القيادة بالمحاكاة باستخدام التغييرات في الموجات الدماغية كمؤشر 
Ahmed Abdelhamid Mohamed Torki ; Supervised Ayman M. Eldeib 
 - Cairo :   Ahmed Abdelhamid Mohamed Torki ,   2018 
 - 79 P. :   charts , facsimiles ;   30cm 
<br/><br/>Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering  
<br/><br/> Driver drowsiness contributes widely in vehicle accidents. Studies assessed drowsiness indicators at different driving setups. The first objective was to establish an experiment to obtain generic datasets of brainwaves epochs recorded by electroencephalogram (EEG) at forehead sites labeled with alertness and drowsiness for subjects using facial expressions recorded by videos. Subjects perform simulated driving for two hours after 6 PM. The second and third objective were to extract and select significant features that yield to highest classification accuracy between alert and drowsy using statistical and classification methods. The results showed the classification accuracy was 85.8 % 
<br/><br/>
<br/><br/><label>Subjects--Index Terms: </label>Driver drowsiness detection Electroencephalogram  Simulated Driving