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Göteborg, Sweden

Shirvany Y.,Chalmers University of Technology | Shirvany Y.,MedTechWest Center | Edelvik F.,Fraunhofer Chalmers Research Center | Jakobsson S.,Fraunhofer Chalmers Research Center | And 3 more authors.
Applied Soft Computing Journal

Surgical therapy has become an important therapeutic alternative for patients with medically intractable epilepsy. Correct and anatomically precise localization of an epileptic focus is essential to decide if resection of brain tissue is possible. The inverse problem in EEG-based source localization is to determine the location of the brain sources that are responsible for the measured potentials at the scalp electrodes. We propose a new global optimization method based on particle swarm optimization (PSO) to solve the epileptic spike EEG source localization inverse problem. In a forward problem a modified subtraction method is proposed to reduce the computational time. The good accuracy and fast convergence are demonstrated for 2D and 3D cases with realistic head models. The results from the new method are promising for use in the pre-surgical clinic in the future. © 2012 Elsevier B.V. All rights reserved. Source

Shirvany Y.,Chalmers University of Technology | Shirvany Y.,MedTechWest Center | Rubaek T.,Technology University of Denmark | Edelvik F.,Fraunhofer Chalmers Research Center | And 5 more authors.
Biomedical Engineering Letters

Purpose: The aim of this paper is to evaluate the performance of an EEG source localization method that combines a finite element method (FEM) and the reciprocity theorem. Methods: The reciprocity method is applied to solve the forward problem in a four-layer spherical head model for a large number of test dipoles. To benchmark the proposed method, the results are compared with an analytical solution and two state-of-the-art methods from the literature. Moreover, the dipole localization error resulting from utilizing the method in the inverse procedure for a realistic head model is investigated with respect to EEG signal noise and electrode misplacement. Results: The results show approximately 3% relative error between numerically calculated potentials done by the reciprocity theorem and the analytical solutions. When adding EEG noise with SNR between 5 and 10, the mean localization error is approximately 4.3 mm. For the case with 10 mm electrode misplacement the localization error is 4.8 mm. The reciprocity EEG source localization speeds up the solution of the inverse problem with more than three orders of magnitude compared to the state-of-the-art methods. Conclusions: The reciprocity method has high accuracy for modeling the dipole in EEG source localization, is robust with respect to noise, and faster than alternative methods. © 2013 Korean Society of Medical and Biological Engineering and Springer. Source

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