NDLI: EEG based brain source localization comparison of sLORETA and eLORETA

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EEG based brain source localization comparison of sLORETA and eLORETA

Content Provider	Semantic Scholar
Author	Jatoi, Munsif Ali Kamel, Nidal S. Malik, Aamir Saeed Faye, Ibrahima
Copyright Year	2014
Abstract	Abstract Human brain generates electromagnetic signals during certain activation inside the brain. The localization of the active sources which are responsible for such activation is termed as brain source localization. This process of source estimation with the help of EEG which is also known as EEG inverse problem is helpful to understand physiological, pathological, mental, functional abnormalities and cognitive behaviour of the brain. This understanding leads for the specification for diagnoses of various brain disorders such as epilepsy and tumour. Different approaches are devised to exactly localize the active sources with minimum localization error, less complexity and more validation which include minimum norm, low resolution brain electromagnetic tomography (LORETA), standardized LORETA, exact LORETA, Multiple Signal classifier, focal under determined system solution etc. This paper discusses and compares the ability of localizing the sources for two low resolution methods i.e., sLORETA and eLORETA respectively. The ERP data with visual stimulus is used for comparison at four different time instants for both methods (sLORETA and eLORETA) and then corresponding activation in terms of scalp map, slice view and cortex map is discussed.
Starting Page	713
Ending Page	721
Page Count	9
File Format	PDF HTM / HTML
DOI	10.1007/s13246-014-0308-3
Alternate Webpage(s)	http://eprints.utp.edu.my/11375/1/AUSTRALASIAN%20PHYSICAL%20&%20ENGINEERING%20SCIENCES%20IN%20MEDICINE%20-%20EEG%20Based%20Brain%20Source%20Localization%20Comparison%20of%20sLORETA%20and%20eLORETA%20-%20Paper.pdf
PubMed reference number	25359588
Alternate Webpage(s)	https://doi.org/10.1007/s13246-014-0308-3
Journal	Medline
Volume Number	37
Journal	Australasian Physical & Engineering Sciences in Medicine
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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