The FieldTrip-SimBio pipeline for EEG forward solutions

Johannes Vorwerk,Carsten H Wolters,Lilla Magyari,Robert Oostenveld,Maria Carla Piastra

doi:10.1186/s12938-018-0463-y

Abstract

BackgroundAccurately solving the electroencephalography (EEG) forward problem is crucial for precise EEG source analysis. Previous studies have shown that the use of multicompartment head models in combination with the finite element method (FEM) can yield high accuracies both numerically and with regard to the geometrical approximation of the human head. However, the workload for the generation of multicompartment head models has often been too high and the use of publicly available FEM implementations too complicated for a wider application of FEM in research studies. In this paper, we present a MATLAB-based pipeline that aims to resolve this lack of easy-to-use integrated software solutions. The presented pipeline allows for the easy application of five-compartment head models with the FEM within the FieldTrip toolbox for EEG source analysis.MethodsThe FEM from the SimBio toolbox, more specifically the St. Venant approach, was integrated into the FieldTrip toolbox. We give a short sketch of the implementation and its application, and we perform a source localization of somatosensory evoked potentials (SEPs) using this pipeline. We then evaluate the accuracy that can be achieved using the automatically generated five-compartment hexahedral head model [skin, skull, cerebrospinal fluid (CSF), gray matter, white matter] in comparison to a highly accurate tetrahedral head model that was generated on the basis of a semiautomatic segmentation with very careful and time-consuming manual corrections.ResultsThe source analysis of the SEP data correctly localizes the P20 component and achieves a high goodness of fit. The subsequent comparison to the highly detailed tetrahedral head model shows that the automatically generated five-compartment head model performs about as well as a highly detailed four-compartment head model (skin, skull, CSF, brain). This is a significant improvement in comparison to a three-compartment head model, which is frequently used in praxis, since the importance of modeling the CSF compartment has been shown in a variety of studies.ConclusionThe presented pipeline facilitates the use of five-compartment head models with the FEM for EEG source analysis. The accuracy with which the EEG forward problem can thereby be solved is increased compared to the commonly used three-compartment head models, and more reliable EEG source reconstruction results can be obtained.

Highlights

Solving the electroencephalography (EEG) forward problem is crucial for precise EEG source analysis
The presented pipeline facilitates the use of five-compartment head models with the finite element method (FEM) for EEG source analysis
The accuracy of EEG source analysis directly depends on the accuracy that is achieved in solving the EEG forward problem

Summary

Introduction

Solving the electroencephalography (EEG) forward problem is crucial for precise EEG source analysis. In many applications of electroencephalography (EEG), it is desirable to reconstruct the active brain areas that generate the measured signals to achieve a better understanding of the neural processes. The reconstruction of these sources is called EEG source analysis; this reconstruction can be split into two mathematical problems, the EEG forward and the EEG inverse problem. Whereas the EEG forward problem consists of simulating the electric potential at the head surface that is generated by a microscopic source of brain activity, the EEG inverse problem aims at reconstructing a distribution of such sources that can explain the measured signal. A detailed derivation of the quasi-static approximation of the EEG forward problem can be found in [1, 2]

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