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Source analysis of Electroencephalography (EEG) data requires the computation of the scalp potential induced by current sources in the brain. This so-called EEG forward problem is based on an accurate estimation of the volume conduction…

We study source localization from high dimensional M/EEG data by extending a multiscale method based on Entropic inference devised to increase the spatial resolution of inverse problems. This method is used to construct informative prior…

Quantitative Methods · Quantitative Biology 2015-02-13 Leonardo S. Barbosa , Nestor Caticha

Electroencephalography (EEG) source imaging aims to infer brain activity from electrical potentials measured on the scalp. This is a difficult problem because many different source patterns can explain the same measurements. The result…

Numerical Analysis · Mathematics 2026-04-29 Santtu Söderholm , Joonas Lahtinen , Sampsa Pursiainen

The quality of the inverse approach in electroencephalography (EEG) source analysis is - among other things - depending on the accuracy of the forward modeling approach, i.e., the simulation of the electric potential for a known dipole…

Medical Physics · Physics 2022-08-08 Florian Drechsler , Johannes Vorwerk , Jens Haueisen , Lars Grasedyck , Carsten H. Wolters

Finite element methods have been shown to achieve high accuracies in numerically solving the EEG forward problem and they enable the realistic modeling of complex geometries and important conductive features such as anisotropic…

Numerical Analysis · Mathematics 2017-08-25 Johannes Vorwerk , Christian Engwer , Sampsa Pursiainen , Carsten H. Wolters

Electroencephalography (EEG) source imaging aims to reconstruct the spatial distribution of neural activity within the brain from non-invasive scalp measurements. This inverse problem is severely ill-posed due to the low spatial resolution…

Numerical Analysis · Mathematics 2026-04-08 Joonas Lahtinen , Alexandra Koulouri

In this paper we present a new discretization strategy for the boundary element formulation of the Electroencephalography (EEG) forward problem. Boundary integral formulations, classically solved with the Boundary Element Method (BEM), are…

Medical Physics · Physics 2016-03-22 Lyes Rahmouni , Simon Adrian , Kristof Cools , Francesco P. Andriulli

MEG and EEG are noninvasive functional neuroimaging techniques that provide recordings of brain activity with high temporal resolution, and thus provide a unique window to study fast time-scale neural dynamics in humans. However, the…

Applications · Statistics 2015-11-13 Camilo Lamus , Matti S. Hamalainen , Emery N. Brown , Patrick L. Purdon

MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-posed inverse problem. Converging lines of…

The goal of this study is to develop focal, accurate and robust finite element method (FEM) based approaches which can predict the electric potential on the surface of the computational domain given its structure and internal primary source…

Medical Physics · Physics 2016-12-21 Sampsa Pursiainen , Johannes Vorwerk , Carsten H. Wolters

Magnetoencephalography (MEG) is an imaging technique used to measure the magnetic field outside the human head produced by the electrical activity inside the brain. The MEG inverse problem, identifying the location of the electrical sources…

Computation · Statistics 2014-08-01 Zhigang Yao , William F. Eddy

Magnetoencephalography (MEG) and electroencephalogra-phy (EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Inferring the location of the current sources that generated these…

Machine Learning · Statistics 2019-02-14 Hicham Janati , Thomas Bazeille , Bertrand Thirion , Marco Cuturi , Alexandre Gramfort

We study the distribution of brain source from the most advanced brain imaging technique, Magnetoencephalography (MEG), which measures the magnetic fields outside the human head produced by the electrical activity inside the brain. Common…

Applications · Statistics 2019-08-13 Zhigang Yao , Zengyan Fan , Masahito Hayashi , William F. Eddy

The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take…

Machine Learning · Computer Science 2020-02-04 Alexandra Koulouri , Ville Rimpilainen

Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Estimating the location and magnitude of the current sources that generated…

Machine Learning · Statistics 2019-10-16 Hicham Janati , Thomas Bazeille , Bertrand Thirion , Marco Cuturi , Alexandre Gramfort

We present a novel solution to the problem of localizing magnetoencephalography (MEG) and electroencephalography (EEG) brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares criterion by the…

Signal Processing · Electrical Eng. & Systems 2022-02-03 Amir Adler , Mati Wax , Dimitrios Pantazis

We present a novel solution to the problem of localization of MEG and EEG brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares (LS)criterion by the Alternating Projection (AP) algorithm,…

Signal Processing · Electrical Eng. & Systems 2020-11-26 Amir Adler , Mati Wax , Dimitrios Pantazis

Electroencephalography (EEG) and Magnetoencephalography (MEG) are pivotal in understanding brain activity but are limited by their poor spatial resolution. EEG/MEG source imaging (ESI) infers the high-resolution electric field distribution…

Signal Processing · Electrical Eng. & Systems 2024-02-01 Song Wang , Chen Wei , Kexin Lou , Dongfeng Gu , Quanying Liu

Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume that…

Applications · Statistics 2019-06-07 Feng Liu , Li Wang , Yifei Lou , Rencang Li , Patrick Purdon

Accurate electroencephalography (EEG) and magnetoencephalography (MEG) source localization and reconstruction are essential for understanding brain function, yet remain challenging because the underlying EEG/MEG inverse problem is…

Optimization and Control · Mathematics 2026-04-29 Julia Jurkowska , Joanna Dreszer , Monika Lewandowska , Krzysztof Tołpa , Tomasz Piotrowski
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