Yazdan Shirvany

Particle Swarm Optimization Applied to EEG Source Localization of Somatosensory Evoked Potentials

One of the most important steps in presurgical diagnosis of medically intractable epilepsy is to find the precise location of the epileptogenic foci. Electroencephalography (EEG) is a noninvasive tool commonly used at epilepsy surgery centers for presurgical diagnosis. In this paper, a modified particle swarm optimization (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 subjects 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 independently identified the expected source location, further corroborating 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.

Application of particle swarm optimization in epileptic spike EEG source localization

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.

Non-invasive EEG source localization using particle swarm optimization: A clinical experiment

One of the most important steps of pre-surgical diagnosis in patients with medically intractable epilepsy is to find the precise location of the epileptogenic foci. An Electroencephalography (EEG) is a non-invasive standard tool used at epilepsy surgery center for pre-surgical diagnosis. In this paper a modified particle swarm optimization (MPSO) method is applied to a real EEG data, i.e., a somatosensory evoked potentials (SEPs) measured from a healthy subject, to solve the EEG source localization problem. A high resolution 1 mm hexahedra finite element volume conductor model of the subjects head was generated using T1-weighted magnetic resonance imaging data. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEPs data. The non-invasive EEG source analysis methods localized the somatosensory cortex area where our clinical expert expected the received SEPs. The proposed inverse problem solver found the global minima with acceptable accuracy and reasonable number of iterations.

Investigation of brain tissue segmentation error and its effect on EEG source localization

Surgical therapy has become an important therapeutic alternative for patients with medically intractable epilepsy. Correct and anatomically precise localization of the epileptic focus, preferably with non-invasive methods, is the main goal of the pre-surgical epilepsy diagnosis to decide if resection of brain tissue is possible. For evaluating the performance of the source localization algorithms in an actual clinical situation, realistic patient-specific human head models that incorporate the heterogeneity nature of brain tissues is required. In this paper, performance of two of the most widely used software packages for brain segmentation, namely FSL and FreeSurfer has been analyzed. Then a segmented head model from a package with better performance is used to investigate the effects of brain tissue segmentation in EEG source localization.

Advances in neuro diagnostics based on microwave technology, transcranial magnetic stimulation and EEG source localization

Advances in neuro diagnostics based on microwave antenna system in terms of a helmet including a set of broad band patch antennas is presented. It is shown that classification algorithms can be used to detect internal bleeding in stroke patients. Transcranial magnetic stimulation has traditionally been used for brain mapping and treatment of depression. In this paper we discuss the use of the method for neuro diagnostics with the help of integrated image guidance. Surgical therapy has become an important therapeutic alternative for some patients with medically intractable epilepsy. Electroencephalography and the associated model based diagnostics as a non-invasive diagnostic tool is also discussed.

Multi-dipole EEG source localization using particle swarm optimization

The multi-dipole EEG source localization problem is (usually) highly nonlinear with a non-convex cost function. Moreover, the gray matter tissue is located in several disjunct regions in the head which leads to a non-continuous solution space. For solving this problem an efficient algorithm which can handle multi-source activities is needed. In this paper, a modified particle swarm optimization (MPSO) method is proposed to solve the multi-dipole EEG source localization. The method is tested on synthetic EEG signals generated from two strong active sources and a noisy background source. The results show that using the new method is a reliable choice when we deal with a strong multi-active source scenario, in which a single dipole source localization may fail.

On the fully automatic construction of a realistic head model for EEG source localization

Accurate multi-tissue segmentation of magnetic resonance (MR) images is an essential first step in the construction of a realistic finite element head conductivity model (FEHCM) for electroencephalography (EEG) source localization. All of the segmentation approaches proposed to date for this purpose require manual intervention or correction and are thus laborious, time-consuming, and subjective. In this paper we propose and evaluate a fully automatic method based on a hierarchical segmentation approach (HSA) incorporating Bayesian-based adaptive mean-shift segmentation (BAMS). An evaluation of HSA-BAMS, as well as two reference methods, in terms of both segmentation accuracy and the source localization accuracy of the resulting FEHCM is also presented. The evaluation was performed using (i) synthetic 2D multi-modal MRI head data and synthetic EEG (generated for a prescribed source), and (ii) real 3D T1-weighted MRI head data and real EEG data (with expert determined source localization). Expert manual segmentation served as segmentation ground truth. The results show that HSA-BAMS outperforms the two reference methods and that it can be used as a surrogate for manual segmentation for the construction of a realistic FEHCM for EEG source localization.

Influence of Different Sources of Noise on Epileptic Spike EEG Source Localization

Spike EEG source localization results are influenced by different errors and approximations, e.g., head-model complexity, EEG signal noise, electrode misplacements, tissue anisotropy, tissue conductivity noise as well as numerical errors. For accurate source localization, understanding the affects of these errors on the source localization is very crucial. Six finite element head models are selected for a head-model complexity study. A reference head model is used to create the synthetic EEG signals by placing a dipole inside the model to mimic the epileptic spike activity. To understand the influence of EEG signal noise, tissue conductivity noise and electrode misplacements on the EEG source localization, different level of noises are added to EEG signals, tissue conductivities and electrode positions, independently. To investigate the influence of white matter anisotropy, a realistic head model generated from T1-weighted MRI is used and the conductivity anisotropy for the white matter is calculated from diffusion tensor imaging (DTI). Major findings of the study include (1) the CSF layer plays an important role to achieve an accurate source localization result, (2) the source localization is very sensitive to the tissue conductivity noises, (3) one centimeter electrode misplacement cause approximately 8 mm localization error, (4) the source localization is robust with respect to the EEG signal noise and (5) the model with white matter anisotropy has small source localization error but large amplitude and orientation errors compared to the isotropic head model.