English
Related papers

Related papers: Comparison of inverse problem linear and non-linea…

200 papers

Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is…

Applications · Statistics 2016-07-29 Daniel Strohmeier , Yousra Bekhti , Jens Haueisen , Alexandre Gramfort

Purpose: Localizing the sources of electrical activity from electroencephalographic (EEG) data has gained considerable attention over the last few years. In this paper, we propose an innovative source localization method for EEG, based on…

Quantitative Methods · Quantitative Biology 2015-01-21 Sajib Saha , Frank de Hoog , Ya. I. Nesterets , Rajib Rana , M. Tahtali , T. E. Gureyev

This paper is concerned with variational and Bayesian approaches to neuro-electromagnetic inverse problems (EEG and MEG). The strong indeterminacy of these problems is tackled by introducing sparsity inducing regularization/priors in a…

Signal Processing · Electrical Eng. & Systems 2023-06-28 Samy Mokhtari , Jean-Michel Badier , Christian G. Bénar , Bruno Torrésani

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

Magnetoencephalography (MEG) is an advanced imaging technique used to measure the magnetic fields outside the human head produced by the electrical activity inside the brain. Various source localization methods in MEG require the knowledge…

Methodology · Statistics 2017-07-06 Zhigang Yao , Ye Zhang , Zhidong Bai , William F. Eddy

Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The…

Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution for inferring a latent source space of neural activity. In this paper we address this inference problem within the…

Machine Learning · Computer Science 2020-10-06 Xueqing Liu , Linbi Hong , Paul Sajda

In this paper, we propose a novel source model for a magnetoencephalography (MEG) inverse problem that combines a conventional extended parametric approach and an imaging approach.Our aim is to separately identify a focal current source and…

Signal Processing · Electrical Eng. & Systems 2023-02-27 Takaaki Nara , Ten-yu Yang , Kenta Kabashima

Subcortical structures play a critical role in brain function. However, options for assessing electrophysiological activity in these structures are limited. Electromagnetic fields generated by neuronal activity in subcortical structures can…

Detecting where and when brain regions activate in a cognitive task or in a given clinical condition is the promise of non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG). This problem, referred to as…

Machine Learning · Statistics 2020-11-26 Jérôme-Alexis Chevalier , Alexandre Gramfort , Joseph Salmon , Bertrand Thirion

The EEG source localization is an ill-posed problem. It involves estimation of the sources which outnumbers the number of measurements. For a given measurement at given time all sources are not active which makes the problem as sparse…

Signal Processing · Electrical Eng. & Systems 2022-02-02 Teja Mannepalli , Aurobinda Routray

Background: Transcranial magnetic stimulation (TMS) is a powerful tool to investigate neurophysiology of the human brain and treat brain disorders. Traditionally, therapeutic TMS has been applied in a one-size-fits-all approach,…

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical…

Neurons and Cognition · Quantitative Biology 2016-08-18 Mahmoud Hassan , Isabelle Merlet , Ahmad Mheich , Aya Kabbara , Arnaud Biraben , Anca Nica , Fabrice Wendling

State-space models are widely employed across various research disciplines to study unobserved dynamics. Conventional estimation techniques, such as Kalman filtering and expectation maximisation, offer valuable insights but incur high…

Computation · Statistics 2023-11-28 Jose M. Sanchez-Bornot , Roberto C. Sotero , Scott Kelso , Damien Coyle

Magnetoencephalographic (MEG) measurements record magnetic fields generated from neurons while information is being processed in the brain. The inverse problem of identifying sources of biomagnetic fields and deducing their intensities from…

Neurons and Cognition · Quantitative Biology 2009-11-11 Hung-I Pai , Chih-Yuan Tseng , HC Lee

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

The problem of reconstructing brain activity from electric potential measurements performed on the surface of a human head is not an easy task: not just because the solution of the related inverse problem is fundamentally ill-posed (not…

Numerical Analysis · Mathematics 2023-08-11 Santtu Söderholm , Joonas Lahtinen , Carsten H. Wolters , Sampsa Pursiainen

Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution. Challenging has been developing principled and interpretable approaches for fusing the modalities, specifically…

Neurons and Cognition · Quantitative Biology 2022-12-06 Xueqing Liu , Tao Tu , Paul Sajda

Electroencephalography (EEG) is a widely used non-invasive technique for measuring brain activity in brain-computer interface (BCI) applications. Supervised EEG decoding models often struggle to generalize across tasks, subjects, and…

Artificial Intelligence · Computer Science 2026-05-29 Ayse Betul Yuce , Sebastian Stober

This paper deals with the EEG/MEG neuroimaging problem: given measurements of scalp electric potential differences (EEG: electroencephalogram) and extracranial magnetic fields (MEG: magnetoencephalogram), find the 3D distribution of the…

Mathematical Physics · Physics 2007-10-18 Roberto D. Pascual-Marqui