scholarly journals Spatio-temporal EEG source localization using a three-dimensional subspace FINE approach in a realistic geometry inhomogeneous head model

2006 ◽  
Vol 53 (9) ◽  
pp. 1732-1739 ◽  
Author(s):  
Lei Ding ◽  
Bin He
Keyword(s):  
Source Localization ◽  
Three Dimensional ◽  
Dimensional Subspace ◽  
Head Model ◽  
Eeg Source Localization ◽  
Spatio Temporal ◽  
Realistic Geometry

scholarly journals Evaluating the performance of BSBL methodology for EEG source localization on a realistic head model

2017 ◽  
Vol 27 (1) ◽  
pp. 46-56 ◽  
Author(s):  
Sajib Saha ◽  
Rajib Rana ◽  
Yakov Nesterets ◽  
Murat Tahtali ◽  
Frank de Hoog ◽  
...  
Keyword(s):  
Source Localization ◽  
Head Model ◽  
Realistic Head Model ◽  
Eeg Source Localization

scholarly journals High-Resolution EEG Source Localization in Segmentation-Free Head Models Based on Finite-Difference Method and Matching Pursuit Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Takayoshi Moridera ◽  
Essam A. Rashed ◽  
Shogo Mizutani ◽  
Akimasa Hirata
Keyword(s):  
Inverse Problem ◽  
High Resolution ◽  
Finite Difference ◽  
Finite Difference Method ◽  
Source Localization ◽  
Difference Method ◽  
Computational Cost ◽  
Head Model ◽  
Eeg Source Localization ◽  
The Brain

Electroencephalogram (EEG) is a method to monitor electrophysiological activity on the scalp, which represents the macroscopic activity of the brain. However, it is challenging to identify EEG source regions inside the brain based on data measured by a scalp-attached network of electrodes. The accuracy of EEG source localization significantly depends on the type of head modeling and inverse problem solver. In this study, we adopted different models with a resolution of 0.5 mm to account for thin tissues/fluids, such as the cerebrospinal fluid (CSF) and dura. In particular, a spatially dependent conductivity (segmentation-free) model created using deep learning was developed and used for more realist representation of electrical conductivity. We then adopted a multi-grid-based finite-difference method (FDM) for forward problem analysis and a sparse-based algorithm to solve the inverse problem. This enabled us to perform efficient source localization using high-resolution model with a reasonable computational cost. Results indicated that the abrupt spatial change in conductivity, inherent in conventional segmentation-based head models, may trigger source localization error accumulation. The accurate modeling of the CSF, whose conductivity is the highest in the head, was an important factor affecting localization accuracy. Moreover, computational experiments with different noise levels and electrode setups demonstrate the robustness of the proposed method with segmentation-free head model.

scholarly journals Consistency of EEG source localization and connectivity estimates

2016 ◽  
Author(s):  
Keyvan Mahjoory ◽  
Vadim V. Nikulin ◽  
Loïc Botrel ◽  
Klaus Linkenkaer-Hansen ◽  
Marco M. Fato ◽  
...  
Keyword(s):  
Source Localization ◽  
Effective Connectivity ◽  
Head Model ◽  
Source Imaging ◽  
The Face ◽  
Software Implementations ◽  
Inverse Solutions ◽  
Eeg Source Localization ◽  
Eeg Recordings ◽  
Forward And Inverse Modeling

AbstractAs the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure.

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