Mahmoud Eid Abdelhafez Abdellahi Abdelhadi  <br/><br/>
Motor imagery detection and classification techniques based on EEG signals from brains /  
 اكتشاف و تصنيف الرغبة في الحركة اعتمادا على الاشارات الكهربية من المخ 
Mahmoud Eid Abdelhafez Abdellahi Abdelhadi ; Supervised Reda Abdelwahab Ahmed Alkhoribi , Mahmoud Ahmed Ismail Shoman , Mohammed Ahmed Ahmed Refaey 
 - Cairo :   Mahmoud Eid Abdelhafez Abdellahi Abdelhadi ,   2016 
 - 125 Leaves :   charts , facsimiles , photographs ;   30cm 
<br/><br/>Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Technology  
<br/><br/> Self-paced Brain Computer Interfaces (BCIs) is a preponderant trend  nowadays for the most natural human-machine interaction. There is an  increasing need to deal with critical disorders that involve death of neurons  namely Amyotrophic Lateral Sclerosis (ALS), or brainstem stroke.  Translating motor imagery activities of the brain can help any patient who  suffers from these severe conditions. The detection of motor imagery is  followed by a classification process. Results can be sent to a computer  program or a wheel chair or a mind controlled prosthesis. Translating motor  imagery into a decision requires a number of parameters to be optimized.  These parameters were found varying from subject to another. Reliable  features have to be extracted that describe the Motor Imagery (MI) related  activity properly  
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<br/><br/><label>Subjects--Index Terms: </label>Brains  EEG signals  Motor imagery