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2010 (Vol. 2, Issue: 2)
Article Information:

Non- Linear Principal Component Analysis Neural Network for Blind Source Separation of EEG Signals

Saad A. Al-Shaban, Muaid S. Al-Faysale and Auns Qusai H. Al- Neami
Corresponding Author:  Saad Alshaban 

Key words:  BSS, EEG, ECG Signals, neural networks, PCA, ,
Vol. 2 , (2): Page No: 180-190
Submitted Accepted Published
2009 November, 17 2009 December, 14 2010 March, 10

The complex system such as human brain generates electrical recording activity from thousands of neurons in the brain. This activity is given as electroencephalogram (EEG) waveforms. The EEG potentials represent the combined effect of potentials from a fairly wide region of the skull's skin (scalp). Mixing some underlying components of brain activity presumably generates these potentials. The mixing of brain fields at the scalp is basically linear mixture. The present study aims to design and implement an unsupervised neurocomputing model for separating the original components of brain activity waveforms from their linear mixture, without further knowledge about their probability distributions and mixing coefficients. This is called the problem of "Blind Source Separation "(BSS). It consists of the recovery of unobservable original independent sources from several observed (mixed) data masked by linear mixing of the sources, when nothing is known about the sources and the mixture structure. The current study used recently developed source separation method known as "Independent Component Analysis" (ICA) technique for solving blind EEG source separation problem. The ICA is used to decompose the observed data into components that are as statistically independent from each other as possible. The ICA algorithm that was used for linear BSS problem is the Nonlinear Principal Component Analysis (BSS) algorithm. The proposed ICA BSS model was implemented using the Matlab version6.1 package. The measured real EEG data signals obtained from normal and abnormal states from the (Neurosurgery Hospital) in Baghdad. The results of the present work show the good performance of the proposed model in separating the m ixed signals. Since the present ICA model is a reliable, robust and effective unsupervised learning model which, enable us to separate the EEG signals from their linear observation records, and extract several specific brain source signals that are potentially interesting and contain useful information that help physician to diagnose the abnormality of the brain easily.
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  Cite this Reference:
Saad A. Al-Shaban, Muaid S. Al-Faysale and Auns Qusai H. Al- Neami, 2010. Non- Linear Principal Component Analysis Neural Network for Blind Source Separation of EEG Signals.  Research Journal of Applied Sciences, Engineering and Technology, 2(2): Page No: 180-190.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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