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واجهات التخاطب بين الدماغ والحاسب
2024
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Created by
Alaa Joukhadar
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واجهة التخاطب بين الحاسب والدماغ (BCI)هي آخر تطورات واجهة التخاطب بين الحاسب والانسان (HCI). فعلى عكس أدوات الادخال التقليدية (لوحة المفاتيح، الفأرة....) تقوم الـ (BCI) بقراءة إشارات الدماغ من مناطق مختلفة من رأس الإنسان وتترجم هذه الإشارات إلى أوامر تستطيع التحكم بالحاسب. تكمن أهمية الـ (BCI) الكثير من التطبيقات كالتطبيقات الطبية وخاصة لمساعد الأشخاص المعاقين كمساعدتهم على التعامل مع أجهزة الحاسوب، أو مساعدة الأشخاص المصابين بـ Locked-In Syndrome على التواصل مع العالم الخارجي، وتطبيقات في الدعاية إذ يتم معرفة مدى إعجاب الزبون بالمنتج، وتطبيقات تعليمية وتطبيقات أمنية، أو أيجاد طريق جديدة للعب الألعاب باستخدام الدماغ. الهدف من هذا البحث هو بيان أجدد الحلول للمشاكل التي تواجهها واجهات التخاطب بين الحاسب والدماغ والخوارزميات المستخدمة لتصنيف إشارات الدماغ. وتكمن صعوبة هذا النوع من الأبحاث في صعوبة استخراج الإشارة ومعالجتها لتصبح قابلة للتصنيف.
The Brain Computer Interface (BCI) is considered the latest development of the Human Computer Interface (HCI). Unlike traditional input devices (keyboard, mouse, etc.) BCI reads brain signals from different areas of the human head and translates these signals into commands that can control the computer. The importance of BCI comes from its many applications such as medical applications, especially to assist people with disabilities to help them deal with computers, and help people with Locked-In Syndrome to communicate with the outside world. and advertising applications to see how much the customer appreciates the product, Security applications, or finding a new way to play games using your brain. The aim of this research is to demonstrate the most recent solutions to the problems faced by computer-brain interfaces and the algorithms used to classify brain signals. The difficulty of this research is the in extracting and processing the signal.
References used
D. K. Abhijeet Mallick, "A Review on Signal Pre-processing Techniques in Brain Computer Inrterface," 2015.
S. R. M. A. E. R. A. A. Rabie A. Ramadan, "Basics of Brain Computer Interface," 2015.
Z. S. Z. M. B. Nelly Elsayed, "Brain Computer Interface EEG Signal Preprocessing Issues and Solutions," 2017.
T. M. N. S. T. K. J. D. John Thomas, "Deep Learning-based Classification for Brain-Computer Interfaces," 2017.
A. Sharmila, "Signal Processing Algorithm for Brain Computer Interface - A Review," 2017.
R. K. W. G. E. B. A. B. Hossein Bashashati, "Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces".
N. A. E. A. I. G. A. Ç. Rahib H. Abiyev, "Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks," 2016.
R. B. R. L. M. H. M. S. Anatole Lécuyer and Fabien Lotte, "Brain-Computer Interfaces, Virtual Reality, and Videogames," 2014.
J. S. K. C. K. C. Hong Gi Yeom, "Common Neural Mechanism for Reaching Movements," 2015.
Y. C. S. F. C. W. Taeho Kang, "Decoding of Human Memory Formation with EEG signals using Convolutional Networks," 2017.
C.-H. I. Chang-Hee Han, "EEG-based Brain-Computer Interface for Real-Time Communication of Patients in Completely Locked-in State," 2018.
J. P. P. O. A. S. C. L. D. R. J. K. L. H. A. P. A. I. S. Gernot R. Müller-Putz, "Towards Non-Invasive Brain-Computer Interface for Hand/Arm Control in Users With Spinal Cord Injury".
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