Farid Ali Mousa Abdelkader <br/><br/>
Machine learning techniques for analyzing brain signals /  
 تطبيقات تعليم الآلة  لتحليل إشارات المخ 
Farid Ali Mousa Abdel Kader ; Supervised Reda Abdel Wahab Elkhoribi , Mahmoud Ismail Shoman 
 - Cairo :   Farid Ali Mousa Abdelkader ,   2016 
 - 122 Leaves :   charts , facsimiles ;   30cm 
<br/><br/>Thesis (Ph.D.) - Cairo University - Faculty of Computers and Information - Department of Information Technology 
<br/><br/> The purpose behind this research is to improve a model for brain signals analysis. Different techniques have been developed in the literature for the classification of brain signals. The purpose of this work is to develop novel methods of analyzing the brain signals. We developed four experiments that used to classify brain. We have used artificial neural networks, support vector machine, k-nearest neighbor, fuzzy k-nearest neighbor, weighted k-nearest neighbor in the classification step. It has been depicted from results that the proposed integrated techniques outperform a better performance than methods mentioned in literature 
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<br/><br/><label>Subjects--Index Terms: </label>Brain computer interface K-Nearest neighbor Principle component analysis