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In this paper we propose spatial filters for a linear regression model which are based on the minimum-variance pseudo-unbiased reduced-rank estimation (MV-PURE) framework. As a sample application, we consider the problem of reconstruction…

Signal Processing · Electrical Eng. & Systems 2024-08-02 Tomasz Piotrowski , Jan Nikadon , David Gutierrez

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

Magnetoencephalography (MEG) provides dynamic spatial-temporal insight of neural activities in the cortex. Because the number of possible sources is far greater than the number of MEG detectors, the proposition to localize sources directly…

Quantitative Methods · Quantitative Biology 2009-03-06 Hung-I Pai , Chih-Yuan Tseng , H. C. Lee

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…

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

Magnetoencephalography (MEG) is a noninvasive method for measuring magnetic flux signals caused by brain activity using sensor arrays located on or above the scalp. A common strategy for monitoring brain activity is to place sensors on a…

Medical Physics · Physics 2022-05-24 Wan-Jin Yeo , Samu Taulu , J. Nathan Kutz

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

Current non-invasive neuroimaging techniques trade off between spatial resolution and temporal resolution. While magnetoencephalography (MEG) can capture rapid neural dynamics and functional magnetic resonance imaging (fMRI) can spatially…

Neurons and Cognition · Quantitative Biology 2025-10-13 Beige Jerry Jin , Leila Wehbe

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

Localizing the sources of electrical activity in the brain from Electroencephalographic (EEG) data is an important tool for non-invasive study of brain dynamics. Generally, the source localization process involves a high-dimensional inverse…

Quantitative Methods · Quantitative Biology 2014-06-11 S. Saha , Ya. I. Nesterets , Rajib Rana , M. Tahtali , Frank de Hoog , T. E. Gureyev

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

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

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

This report introduces a new hierarchical Bayesian model for the EEG source localization problem. This model promotes structured sparsity to search for focal brain activity. This sparsity is obtained via a multivariate Bernoulli Laplacian…

Methodology · Statistics 2015-09-16 Facundo Costa , Hadj Batatia , Thomas Oberlin , Jean-Yves Tourneret

We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned for single and multiple time point MEG data, and can estimate varying numbers…

Signal Processing · Electrical Eng. & Systems 2020-12-02 Dimitrios Pantazis , Amir Adler

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

The Electro-Encephalo-Graphy (EEG) technique consists of estimating the cortical distribution of signals over time of electrical activity and also of locating the zones of primary sensory projection. Moreover, it is able to record…

Signal Processing · Electrical Eng. & Systems 2021-12-02 Ridha jarray , Abir Hadriche , Cokri ben Amar , Nawel Jmail

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

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

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
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