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Nonlinear Blind Source Separation for EEG Signal Pre-processing in Brain-Computer Interface System for Epilepsy

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 14
Year of Publication: 2012
Authors:
D. A. Torse
R. R. Maggavi
S. A. Pujari
10.5120/7838-0911

D A Torse, R R Maggavi and S A Pujari. Article: Nonlinear Blind Source Separation for EEG Signal Pre-processing in Brain-Computer Interface System for Epilepsy. International Journal of Computer Applications 50(14):12-19, July 2012. Full text available. BibTeX

@article{key:article,
	author = {D. A. Torse and R. R. Maggavi and S. A. Pujari},
	title = {Article: Nonlinear Blind Source Separation for EEG Signal Pre-processing in Brain-Computer Interface System for Epilepsy},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {14},
	pages = {12-19},
	month = {July},
	note = {Full text available}
}

Abstract

Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) potentials represent the combined effect of potentials from a fairly wide region of the scalp. Mixing some underlying components of brain activity apparently generates these potentials. The aim of the present study is to separate the original components of brain activity waveforms from their linear mixture. The probability distributions and mixing coefficients knowledge is not considered. This is called the problem of "Nonlinear Blind Source Separation" (NBSS). It consists of the recovery of unobservable original independent components from several mixed components covered by mixed sources. The current study used recently developed source separation method known as "Independent Component Analysis" (ICA) technique for solving blind EEG source separation problem. The proposed ICA NBSS model has been implemented using the Matlab version 7. 7. The measured real EEG data signals obtained from epileptic states. The results of the present work show the good performance of the proposed model in separating the mixed signals.

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