MNE software for processing MEG and EEG data

Interpreting and controlling bioelectromagnetic phenomena require realistic physiological models and accurate numerical solvers. A semi-realistic model often used in practise is the piecewise constant conductivity model, for which only the interfaces have to be meshed. This simplified model makes it possible to use Boundary Element Methods. Unfortunately, most Boundary Element solutions are confronted with accuracy issues when the conductivity ratio between neighboring tissues is high, as for instance the scalp/skull conductivity ratio in electro-encephalography. To overcome this difficulty, we proposed a new method called the symmetric BEM, which is implemented in the OpenMEEG software. The aim of this paper is to present OpenMEEG, both from the theoretical and the practical point of view, and to compare its performances with other competing software packages. We have run a benchmark study in the field of electro- and magneto-encephalography, in order to compare the accuracy of OpenMEEG with other freely distributed forward solvers. We considered spherical models, for which analytical solutions exist, and we designed randomized meshes to assess the variability of the accuracy. Two measures were used to characterize the accuracy. the Relative Difference Measure and the Magnitude ratio. The comparisons were run, either with a constant number of mesh nodes, or a constant number of unknowns across methods. Computing times were also compared. We observed more pronounced differences in accuracy in electroencephalography than in magnetoencephalography. The methods could be classified in three categories: the linear collocation methods, that run very fast but with low accuracy, the linear collocation methods with isolated skull approach for which the accuracy is improved, and OpenMEEG that clearly outperforms the others. As far as speed is concerned, OpenMEEG is on par with the other methods for a constant number of unknowns, and is hence faster for a prescribed accuracy level. This study clearly shows that OpenMEEG represents the state of the art for forward computations. Moreover, our software development strategies have made it handy to use and to integrate with other packages. The bioelectromagnetic research community should therefore be able to benefit from OpenMEEG with a limited development effort.

/pdf/openmeeg-opensource-software-for-quasistatic-loqtt02qjq.pdf

OpenMEEG: opensource software for quasistatic bioelectromagnetics

https://biblio.ugent.be/publication/8650534/file/8672654.pdf

Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014–2018)

Coronary artery disease

In the single authored, expert voice that is this book's unique strength, Arnold Katz provides a comprehensive overview of the physiological and biophysical basis of cardiac function, beginning with structure and proceeding to biochemistry, biophysics, and pathophysiology in arrhythmias, ischemia, and heart failure. Emphasis is on the interrelationships of basic processes among the cell, cardiac muscle function, and the biophysics of contractile and electrical behavior. This edition includes new material on cell signaling and molecular biology.

Physiology of the heart

https://research.aalto.fi/files/11637263/1_s2.0_S1053811916307704_main.pdf

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

A Matlab library for solving quasi-static volume conduction problems using the boundary element method

Comparison of three-shell and simplified volume conductor models in magnetoencephalography.

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.

https://iopscience.iop.org/article/10.1088/0031-9155/57/11/3517/pdf

Matti Stenroos

Papers

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

A Matlab library for solving quasi-static volume conduction problems using the boundary element method

Comparison of three-shell and simplified volume conductor models in magnetoencephalography.

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

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