Estimation of the neuronal current via Electro-Magneto-Encephalography using real data

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dc.contributor.author Hashemzadeh, P.
dc.contributor.author Hauk, O.
dc.contributor.author Fokas, Athanassios S.
dc.date.accessioned 2014-10-11T09:05:18Z
dc.date.available 2014-10-11T09:05:18Z
dc.date.issued 2014-09-02
dc.identifier.isbn 978-960-8475-22-9
dc.identifier.uri http://lib.amcl.tuc.gr/handle/triton/44
dc.description The authors would like to thank EPSRC (Engineering and Physical Research Council) for funding this research. We would like to thank in particular Dr.A Papanicolaou of the division of clinical neurosciences at the University of Tennessee for his advice and discussions. We also like to also thank Dr. M. Stenroos for providing the boundary element mesh and sensor positions. en_US
dc.description.abstract The medical significance of Electroencephalography (EEG), and Magnetoencephalography is well established, see for examples [1–3]. EEG and MEG are considered two of the most important imaging techniques for real time brain imaging. In order to generate images of the brain activation using either EEG or MEG, it is necessary to analyse certain mathematical inverse problems. The definitive answer to the inverse source problem for the case of EEG and MEG was finally obtained by [4]. Here, we present reconstructions of the current using real data via the formulation proposed in [4]. The data was provided by the Medical Research Council (MRC) Cambridge, UK and involves a visual stimuli. We show comparisons of the reconstructed irrotational component of the neuronal current using EEG measurements and the radial component of the neuronal current using MEG measurements. Based on the results, we argue that EEG imaging technology has the potential to become the dominant real time, low cost brain imaging tool. en_US
dc.description.sponsorship EPSRC en_US
dc.language.iso en en_US
dc.publisher AMCL/TUC en_US
dc.subject MEG en_US
dc.subject EEG en_US
dc.subject imaging techniques en_US
dc.title Estimation of the neuronal current via Electro-Magneto-Encephalography using real data en_US
dc.type Article en_US


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  • NumAn2014 Proceedings
    Proceedings of the 6th International Conference on Numerical Analysis (NumAn2014)

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