الفهرس 
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Abstract
Brain computer Interface (BCI) is a communication channel between brain
and computer. It provides communication method between
Electroencephalography (EEG) signals that are detected from the scalp
using signal acquisition device and an exterior device such as (robot arm,
wheelchair…). These applications could help people who are paralyzed,
having other movement disorders and having problems in speech to
provide them with alternative methods for communication and control.
BCIs suffer from many problems including low accuracy, high execution
time, false positive detections and low signal to noise ratio (SNR) of EEG
signals.
The purpose of this thesis is to design an offline brain computer interface
system. This system could translate the human arm movements into
commands to control simulator robot arm to execute the desired
movement. It could also use human mental thoughts to move a cursor on
the screen to form words and sentences according to Morse scheme.
Channel selection was performed to reduce the size of EEG data. Filters
have been used to remove unwanted artifacts. Three different feature
extraction methods were used: Wavelet Transform (WT), Fast Fourier
Transform.