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Accuracy and run-time comparison for different potential approaches and iterative solvers in finite element method based EEG source analysis
- APPLIED NUMERICAL MATHEMATICS
, 2009
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Improved EEG Source Analysis Using Low-Resolution Conductivity Estimation in a Four-Compartment Finite Element Head Model
"... Abstract: Bioelectric source analysis in the human brain from scalp electroencephalography (EEG) signals is sensitive to geometry and conductivity properties of the different head tissues. We propose a low-resolution conductivity estimation (LRCE) method using simulated annealing optimization on hig ..."
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Cited by 3 (2 self)
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Abstract: Bioelectric source analysis in the human brain from scalp electroencephalography (EEG) signals is sensitive to geometry and conductivity properties of the different head tissues. We propose a low-resolution conductivity estimation (LRCE) method using simulated annealing optimization on high-resolution finite element models that individually optimizes a realistically shaped four-layer volume conductor with regard to the brain and skull compartment conductivities. As input data, the method needs T1- and PD-weighted magnetic resonance images for an improved modeling of the skull and the cerebrospinal fluid compartment and evoked potential data with high signal-to-noise ratio (SNR). Our simulation studies showed that for EEG data with realistic SNR, the LRCE method was able to simultaneously reconstruct both the brain and the skull conductivity together with the underlying dipole source and provided an improved source analysis result. We have also demonstrated the feasibility and applicability of the new method to simultaneously estimate brain and skull conductivity and a somatosensory source from measured tactile somatosensory-evoked potentials of a human subject. Our results show the viability of an approach that computes its own conductivity values and thus reduces the dependence on assigning values from the literature and likely produces a more robust estimate of current sources. Using the LRCE method, the individually optimized four-compartment volume conductor model can, in a second step, be used for the analysis of clinical or cognitive data
Coupling of numerical methods for the forward problem in Magneto- and Electro-EncephaloGraphy
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NeuroImage 44 (2009) 399–410 Contents lists available at ScienceDirect
"... journal homepage: www.elsevier.com/locate/ynimg EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra ..."
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journal homepage: www.elsevier.com/locate/ynimg EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra
Max-Plan k-Institut für Mathematik in den Naturwissenschaften Leipzig
"... highly accurate full subtraction approach for dipole modelling in EEG source analysis using the finite element method by ..."
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highly accurate full subtraction approach for dipole modelling in EEG source analysis using the finite element method by
computation and calibration
, 2010
"... (Sciences et Technologies de l’Information et de la Communication) ..."
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Special Finite Elements for Dipole Modelling
"... angegebenen Quellen angefertigt habe und dass die Arbeit in gleicher oder ähnlicher Form noch keiner anderen Prüfungsbehörde vorgelegen hat und von dieser als Teil einer Prüfungsleistung angenommen wurde. Alle Ausführungen, die wörtlich oder sinngemäß übernommen wurden, sind als solche gekennzeichne ..."
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angegebenen Quellen angefertigt habe und dass die Arbeit in gleicher oder ähnlicher Form noch keiner anderen Prüfungsbehörde vorgelegen hat und von dieser als Teil einer Prüfungsleistung angenommen wurde. Alle Ausführungen, die wörtlich oder sinngemäß übernommen wurden, sind als solche gekennzeichnet. This thesis focuses on the solution of the EEG forward problem, using the finite element method. The goal is to compare different dipole models for a current source in the human head, with a focus on Whitney type basis functions. For the current sources in the head a widely used model is the mathematical or current dipole. Its strong singularity poses a problem for numerical methods. Therefore we investigate a less singular dipole model based on Whitney forms. In the first part of the thesis we give an overview of the EEG forward problem and the theory behind Whitney elements. Then we investigate methods for representing a mathematical dipole in Whitney formulation, in order to validate the Whitney approach. The following comparison of the available models on tetrahedral and hexahedral meshes shows, that the Whitney model achieves the highest accuracy if the local mesh
Particle Swarm Optimization Applied to EEG Source Localization of Somatosensory Evoked Potentials
"... Abstract—One of the most important steps in presurgical diag-nosis of medically intractable epilepsy is to find the precise location of the epileptogenic foci. Electroencephalography (EEG) is a non-invasive tool commonly used at epilepsy surgery centers for presur-gical diagnosis. In this paper, a m ..."
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Abstract—One of the most important steps in presurgical diag-nosis of medically intractable epilepsy is to find the precise location of the epileptogenic foci. Electroencephalography (EEG) is a non-invasive tool commonly used at epilepsy surgery centers for presur-gical diagnosis. In this paper, a modified particle swarm optimiza-tion (MPSO) method is used to solve the EEG source localization problem. The method is applied to noninvasive EEG recording of somatosensory evoked potentials (SEPs) for a healthy subject. A 1 mm hexahedra finite element volume conductor model of the sub-ject’s head was generated using T1-weighted magnetic resonance imaging data. Special consideration was made to accurately model the skull and cerebrospinal fluid. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEP data and both identified the same region of the somatosensory cortex as the location of the SEP source. A clinical expert inde-pendently identified the expected source location, further corrob-orating the source analysis methods. The MPSO converged to the global minima with significantly lower computational complexity compared to the exhaustive search method that required almost 3700 times more evaluations. Index Terms—Electroencephalogram (EEG) source localiza-tion, finite element method (FEM), inverse problem, magnetic resonance imaging (MRI), particle swarm optimization, so-matosensory evoked potential (SEP), subtraction method. I.
