Untitled

Abstract

The performance of the finite difference reciprocity method (FDRM) to solve the inverse problem in EEG dipole source analysis is investigated in the analytically solvable three-shell spherical head model for a large set of test dipoles. The location error for a grid with 2 mm and 3 mm node spacing is in general, not larger than twice the internode distance, hence 4 mm and 6 mm, respectively. Increasing the number of scalp electrodes from 27 to 44 only marginally improves the location error. The orientation error is always smaller than 4 degrees for all the test dipoles considered. We have also compared the sensitivity to noise using FDRM in EEG dipole source analysis with the sensitivity to noise using the analytical expression for the forward problem. FDRM is not more sensitive to noise than the method using the analytical expression.

Epileptogenic focus localization in treatment-resistant post-traumatic epilepsy

“…One reason for this is the fact that typical scalp EEG patterns can extend over large portions of the scalp, which usually limits the ability to localize activation sources below the lobar level. By contrast, in the case of anatomically-constrained inverse localization, cortical areas which are electrically-active can be identified as specific portions of gyri and sulci, as documented elsewhere based on validation studies [60–62] and on comparisons of the two approaches [63–65]. …”

Realistic and Spherical Head Modeling for EEG Forward Problem Solution: A Comparative Cortex-Based Analysis

“…For each source position, three single dipole sources have been placed, oriented parallel to the x , y, or z orthogonal Cartesian axes according to the “Talairach” coordinate system, since a source with generic orientation can be always decomposed in its components along the coordinate axes [6]. The study was performed using the numerical FDM for EEG forward problem solution presented in [10], the Galerkin BEM with linear basis algorithm described in [7] for BEM, and analytic calculations for the spherical model [7]. …”

“…The “sensor-fitted sphere” approach fits a multilayer sphere individually to each sensor and has shown to produce some improvement over standard spherical models [7]. More accurate forward solutions become possible by using numerical algorithms, such as the boundary element method (BEM) [8], finite-element method (FEM) [9] and finite difference method (FDM) [10] algorithms. These numerical models allow incorporating the realistic geometry of the head and brain after reconstruction of the anatomical structure from individual or standardized magnetic resonance imaging (MRI) data sets.…”

Abstracts of Presentations at the International Conference on Basic and Clinical Multimodal Imaging (BaCI), a Joint Conference of the International Society for Neuroimaging in Psychiatry (ISNIP), the International Society for Functional Source Imaging (ISFSI), the International Society for Bioelectromagnetism (ISBEM), the International Society for Brain Electromagnetic Topography (ISBET), and the EEG and Clinical Neuroscience Society (ECNS), in Geneva, Swit

“…We optimized a synchronous system for two untrained subjects and investigated two aspects. First, we investigated the effect of using beamformers calculated on the basis of three different head models: a template 3-layered boundary element method (BEM) head model, a 3-layered personalized BEM head model and a personalized 5-layered finite difference method 2 (FDM) head model including white and gray matter, CSF, scalp and skull tissue. Second, we investigated the influence of how the regions of interest, areas of expected MI activity, were constructed.…”