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Document Details
Document Type
:
Thesis
Document Title
:
Speller Classifiers for the P300 Signal of a Brain Computer Interface
مصنفات المتهجيات لإشارة بي 300 في الواجهة بين الدماغ والحاسوب
Subject
:
Faculty of Engineering - Department of Electrical and Computer Engineering
Document Language
:
Arabic
Abstract
:
A Brain Computer Interfaces (BCI) represents a new communication option for those who suffer from neuromuscular impairment that prevents them from using conventional augmented communication methods. In other words, a BCI allows users to act on their environment by using only brain activity, without using peripheral nerves and muscles. This new approach has been developing quickly during the last few years, thanks to the increasing of computational power and the new algorithms for signal processing that can be applied to the studies made on brain waves. It is a communication and control mechanism that has a significant difference to other studies in Human Computer Interface (HCI) as it does not rely on any kind of muscular response to send a message to the external world. This thesis defines BCI technology and displays how brain signals can be recorded using different methods and showing the reason for using P300 Signal rather than other methods. This thesis also focuses on the development of a Word Speller paradigm on P300 which can be used easily by merely concentrating on the letter the user wants to type on screen. In this thesis, many algorithms were used (with / without Principal Component Analysis (PCA) module) for building the Word Speller on P300 of the electrical activity generated by the neurons firing in the brain, electroencephalogram (EEG) and compared them for future implementations. Also, it obtained a good classifier that has a high accuracy of 100%.
Supervisor
:
Dr. Muhammad Shafique Shaikh
Thesis Type
:
Master Thesis
Publishing Year
:
1434 AH
2013 AD
Added Date
:
Wednesday, May 1, 2013
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
عبدالرحمن محمد الفتيح
Alftieh, Abdulrahman Mohammad
Researcher
Master
Files
File Name
Type
Description
35536.pdf
pdf
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