الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
The field of Brain-Computer Interfaces (BCI) is a driving force for utilizing
electroencephalography technology (EEG), which is the process of recording
brain activity from the scalp using electrodes. In the past, the main focus have
been about developing applications in a medical context, helping paralyzed or
disabled patients to interact with the external world by mapping brain signals to
human cognitive and/or sensory- motor functions.
The purpose of this thesis is to prove that the BCI development is no longer
constrained to just patients; but it may be used for disease diagnosis in earlier
stage so that treatment can be more effective.
A non invasive method used for measuring EEG signals by only three electrodes
instead of 25 or 101 electrodes. This method gives accurate results and is more
comfortable for the patients.
In this thesis, Welch Peridogram analysis is used for signal classification. It
applied on the EEG signals measured for patient with Chiari Malformation and
Juvenile Myoclonic Epilepsy. Also, it applied on a normal child with 3 years
old.
For the child, detecting the effect of on off lamp on EEG signal have been
studied, and different brain functions (ex: recognition of a picture for an animal
and searching for a detected animal from a child book). Also for adult person,
effects of eye blinking and laughing on EEG signal have been studied.
Values of absolute and relative power (proportion of power across 4-30 HZ) in 5
frequency bands: 4-7.75 HZ (Theta), 8-9.75 HZ (Alpha1), 10-13 HZ (Alpha2),
13.25-19.75 HZ (Beta 1) and 20-30 HZ (Beta 2) are calculated for each power
spectrum.
The results proved that brain diseases could be reliably diagnosed via the EEG
signal which is more economic and comfortable to patients than the MRI. Also,
different brain functions can be classified using P-Welch Method.