3,188 research outputs found

    Calculation of the Potential Distribution for a Three-Layer Spherical Volume Conductor

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    Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive methods of studying the functional activity of the human brain with millisecond temporal resolution. Much of the work in EEG and MEG in the last few decades has been focused on estimating the properties of the internal sources of the fields from the external measurements, i.e. on solving the inverse problem of EEG and MEG. To handle this task one must first study the forward problem, i.e. how the fields arise from a known source. For practical purposes, one also has to choose appropriate models for the source and the head as a conductor. The most straightforward model for describing the surface evoked potential or the external evoked magnetic field is the single equivalent current dipole. In EEG models the volume conductor properties of the head are commonly modelled by three or four concentric spherical shells with different electrical conductivities representing the brain, the cerebrospinal fluid, the skull, and the scalp. While more accurate geometric models have been applied, such asymmetric models are limited in accuracy by knowledge of boundaries and resistivities of various tissues. In this article we consider a three-layer spherical volume conductor model and calculate the dipole-induced potential by analytical methods. This calculation requires the symbolic solution of a system of linear equations which is not complicated but that would be a pain when done by pencil and paper. We use Maple for setting up the system of model equations, solve it symbolically, and then generate numerical code to obtain a fast program for the evaluation of the potential. Finally, the dipole-envoked electric potential is plotted for realistic EEG model parameters

    Dipole models for the EEG and MEG

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    Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings

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    We deal with estimation of multiple dipoles from combined MEG and EEG time--series. We use a sequential Monte Carlo algorithm to characterize the posterior distribution of the number of dipoles and their locations. By considering three test cases, we show that using the combined data the method can localize sources that are not easily (or not at all) visible with either of the two individual data alone. In addition, the posterior distribution from combined data exhibits a lower variance, i.e. lower uncertainty, than the posterior from single device.Comment: 4 pages, 3 figures -- conference paper from EMBEC 2017, Tampere, Finlan

    Inverse Modeling for MEG/EEG data

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    We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur
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