OpenMEEG: opensource software for quasistatic bioelectromagnetics

Abstract

BackgroundInterpreting 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.MethodsWe 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.ResultsWe 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.ConclusionsThis 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.

Slow-theta power decreases during item-place encoding predict spatial accuracy of subsequent context recall

“…Tissue conductivities were set to 0.33, 0.0041, 1.79 and 0.33 S/m, for scalp, skull, CSF and brain, respective ly. OpenMEEG (Gramfort et al, 2010; http://openmeeg.github.io/), was used to compute a BEM based forward model. To facilitate comparisons of equivalent brain sources across participants, source locations were defined in MNI coordinates using a 5 mm grid covering the brain.…”

An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality

“…In order to validate how the inverse methods are affected by these choices we focus on two different EEG setups, Emotiv EPOC (16 channels) and Biosemi Active-II system (64 channels). For each of the EEG setups we examine three types of head models; 3-spheres (SPM8 toolbox), BEM-CP (SPM8 toolbox) [20], BEM-OP (OpenMEEG toolbox) [21] all using the same cortical surface with a resolution of 5,124 vertices. In contrast to spheres models BEM models face high numerical challenges in order to extract the forward fields at the cortical level.…”

Cloud resource MathBrain for encephalography data analysis

“…Основным назначением программного комплекса OpenMEEG [8,9] является решение прямых задач магнитной и электроэнцефалографии. Программное обеспечение написано на языке программирования C/C++ с использованием библиотеки Intel MKL для ОС Windows и ATLAS (BLAS/LAPACK) для Linux, которые используются для достижения точных расчетов в линейной алгебре.…”