Literature DB >> 22581305

Bioelectromagnetic forward problem: isolated source approach revis(it)ed.

M Stenroos1, J Sarvas.   

Abstract

Electro- and magnetoencephalography (EEG and MEG) are non-invasive modalities for studying the electrical activity of the brain by measuring voltages on the scalp and magnetic fields outside the head. In the forward problem of EEG and MEG, the relationship between the neural sources and resulting signals is characterized using electromagnetic field theory. This forward problem is commonly solved with the boundary-element method (BEM). The EEG forward problem is numerically challenging due to the low relative conductivity of the skull. In this work, we revise the isolated source approach (ISA) that enables the accurate, computationally efficient BEM solution of this problem. The ISA is formulated for generic basis and weight functions that enable the use of Galerkin weighting. The implementation of the ISA-formulated linear Galerkin BEM (LGISA) is first verified in spherical geometry. Then, the LGISA is compared with conventional Galerkin and symmetric BEM approaches in a realistic 3-shell EEG/MEG model. The results show that the LGISA is a state-of-the-art method for EEG/MEG forward modeling: the ISA formulation increases the accuracy and decreases the computational load. Contrary to some earlier studies, the results show that the ISA increases the accuracy also in the computation of magnetic fields.

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Year:  2012        PMID: 22581305     DOI: 10.1088/0031-9155/57/11/3517

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  21 in total

1.  Measuring MEG closer to the brain: Performance of on-scalp sensor arrays.

Authors:  Joonas Iivanainen; Matti Stenroos; Lauri Parkkonen
Journal:  Neuroimage       Date:  2016-12-19       Impact factor: 6.556

2.  Forward and inverse effects of the complete electrode model in neonatal EEG.

Authors:  S Pursiainen; S Lew; C H Wolters
Journal:  J Neurophysiol       Date:  2016-11-16       Impact factor: 2.714

3.  Conditions for numerically accurate TMS electric field simulation.

Authors:  Luis J Gomez; Moritz Dannhauer; Lari M Koponen; Angel V Peterchev
Journal:  Brain Stimul       Date:  2019-10-03       Impact factor: 8.955

4.  Comparative performance of the finite element method and the boundary element fast multipole method for problems mimicking transcranial magnetic stimulation (TMS).

Authors:  Aung Thu Htet; Guilherme B Saturnino; Edward H Burnham; Gregory M Noetscher; Aapo Nummenmaa; Sergey N Makarov
Journal:  J Neural Eng       Date:  2019-01-03       Impact factor: 5.379

5.  Comparison of spherical and realistically shaped boundary element head models for transcranial magnetic stimulation navigation.

Authors:  Aapo Nummenmaa; Matti Stenroos; Risto J Ilmoniemi; Yoshio C Okada; Matti S Hämäläinen; Tommi Raij
Journal:  Clin Neurophysiol       Date:  2013-07-25       Impact factor: 3.708

6.  MNE software for processing MEG and EEG data.

Authors:  Alexandre Gramfort; Martin Luessi; Eric Larson; Denis A Engemann; Daniel Strohmeier; Christian Brodbeck; Lauri Parkkonen; Matti S Hämäläinen
Journal:  Neuroimage       Date:  2013-10-24       Impact factor: 6.556

7.  A Quasi-Static Boundary Element Approach With Fast Multipole Acceleration for High-Resolution Bioelectromagnetic Models.

Authors:  Sergey N Makarov; Gregory M Noetscher; Tommi Raij; Aapo Nummenmaa
Journal:  IEEE Trans Biomed Eng       Date:  2018-03-07       Impact factor: 4.538

8.  Prefrontal Theta-Phase Synchronized Brain Stimulation With Real-Time EEG-Triggered TMS.

Authors:  Pedro Caldana Gordon; Sara Dörre; Paolo Belardinelli; Matti Stenroos; Brigitte Zrenner; Ulf Ziemann; Christoph Zrenner
Journal:  Front Hum Neurosci       Date:  2021-06-21       Impact factor: 3.169

9.  Minimum-norm cortical source estimation in layered head models is robust against skull conductivity error.

Authors:  Matti Stenroos; Olaf Hauk
Journal:  Neuroimage       Date:  2013-04-29       Impact factor: 6.556

10.  Skull Defects in Finite Element Head Models for Source Reconstruction from Magnetoencephalography Signals.

Authors:  Stephan Lau; Daniel Güllmar; Lars Flemming; David B Grayden; Mark J Cook; Carsten H Wolters; Jens Haueisen
Journal:  Front Neurosci       Date:  2016-04-07       Impact factor: 4.677

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