EEG/MEG Source Imaging: Methods, Challenges, and Open Issues

Wendel, Katrina; Väisänen, Outi; Malmivuo, Jaakko; Gençer, N.G.; Vanrumste, Bart; Durka, P.J.; Magjarević, Ratko; Supek, Selma; Pascu, Mihail-Lucian; Fontenelle, Hugues; Menéndez, Rolando Grave de Peralta

doi:10.1155/2009/656092

Cited by 103 publications

(62 citation statements)

References 80 publications

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“…Electroencephalography (EEG) is the non-invasive/invasive functional neuroimaging technique developed to measure brain activity by measuring electrical signals generated with the help of electrodes placed on the scalp [1][2][3][4]. This EEG signal is not pure in form; rather it has different contaminations.…”

Section: Introductionmentioning

confidence: 99%

EEG based brain source localization comparison of sLORETA and eLORETA

Jatoi

Kamel

Malik

et al. 2014

Australas Phys Eng Sci Med

113

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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.

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Section: Introductionmentioning

confidence: 99%

EEG based brain source localization comparison of sLORETA and eLORETA

Jatoi

Kamel

Malik

et al. 2014

Australas Phys Eng Sci Med

113

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“…on the scalp [20] at specific positions where changes of neural patterns are identified [21]. The neurons generate small electrical currents around cell membranes, specifically along the dendrites, and they receive input signals from other neurons [22].…”

Section: Electroencephalographymentioning

confidence: 99%

Neurorestorative Effects of Constraint-Induced Movement Therapy after Stroke: An Integrative Review

Lucas¹,

Ribeiro²,

Cagy³

et al. 2013

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Stroke has been considered as one of the main causes of death and of motor and cognitive sequels. Especially, many patients with upper limb hemiparesis improved their motor action and showed meaningful cortical changes after treatment with constraint-induced movement therapy. Therefore, this review aims to verify the literature about neuroimaging and behavioral evidences in the cortical reorganization through the use of the constraint-induced movement therapy. So, we conducted the literature research in indexed journals from many databases like Pubmed, Medline, Cochrane Database, Lilacs and Scielo. We concluded that the behavioral and neuroimaging studies using traditional and modified constraint-induced movement therapy promote cortical reorganization.

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“…Consequently, these models increase the model complexity in order to reduce errors in source localization, source imaging, and scalp potentials Babiloni et al (1997);Cuffin (1995); Gevins et al (1991); Huiskamp et al (1999); Michel et al (2004). These complex models require numerical solutions such as the boundary element method (BEM), finite element method (FEM), or finite difference method (FDM) (Hallez et al, 2007;Wendel, Väisänen, Malmivuo, Gencer, Vanrumste, Durka, Magjarević, Supek, Pascu, Fontenelle & Grave de Peralta Menendez, 2009). …”

Section: Realistic Individual Modelsmentioning

confidence: 99%