Biomedical subjects

Imam Samil Yetik

Publications and source records attributed to Imam Samil Yetik.

6 recordsLinked to original sources

PubMed2006

Estimating parametric line-source models with electroencephalography.

We develop three parametric models for electroencephalography (EEG) to estimate current sources that are spatially distributed on a line. We assume a realistic head model and solve the EEG forward problem using the boundary element method (BEM). We present the models with increasing degrees of freedom, provide the forward solutions, and derive the maximum-likelihood estimates as well as Cramér-Rao bounds of the unknown source parameters. A series of experiments are conducted to evaluate the applicability of the proposed models. We use numerical examples to demonstrate the usefulness of our line-source models in estimating extended sources. We also apply our models to the real EEG data of N20 response that is known to have an extended source. We observe that the line-source models explain the N20 measurements better than the dipole model.

Action Potentials↗

PubMed2006

Surface-source modeling and estimation using biomagnetic measurements.

We propose a number of electric source models that are spatially distributed on an unknown surface for biomagnetism. These can be useful to model, e.g., patches of electrical activity on the cortex. We use a realistic head (or another organ) model and discuss the special case of a spherical head model with radial sensors resulting in more efficient computations of the estimates for magnetoencephalography. We derive forward solutions, maximum likelihood (ML) estimates, and Cramér-Rao bound (CRB) expressions for the unknown source parameters. A model selection method is applied to decide on the most appropriate model. We also present numerical examples to compare the performances and computational costs of the different models and illustrate when it is possible to distinguish between surface and focal sources or line sources. Finally, we apply our methods to real biomagnetic data of phantom human torso and demonstrate the applicability of them.

Action Potentials↗

PubMed2006

Parametric surface-source modeling and estimation with electroencephalography.

Electroencephalography (EEG) is an important tool for studying the brain functions and is becoming popular in clinical practice. In this paper, we develop four parametric EEG models to estimate current sources that are spatially distributed on a surface. Our models approximate the source shape and extent explicitly and can be applied to localize extended sources which are often encountered, e.g., in epilepsy diagnosis. We assume a realistic head model and solve the EEG forward problem using the boundary element method. We present the source models with increasing degrees of freedom, provide the forward solutions, and derive the maximum-likelihood estimates as well as Cramér-Rao bounds of the unknown source parameters. In order to evaluate the applicability of the proposed models, we first compare their estimation performances with the dipole model's using several known source distributions. We then discuss the conditions under which we can distinguish between the proposed extended sources and the focal dipole using the generalized likelihood ratio test. We also apply our models to the electric measurements obtained from a phantom body in which an extended electric source is imbedded. We observe that the proposed model can capture the source extent information satisfactorily and the localization accuracy is better than the dipole model.

Action Potentials↗

PubMed2005

Distinguishing between moving and stationary sources using EEG/MEG measurements with an application to epilepsy.

Performances of electroencephalography (EEG) and magnetoencephalography (MEG) source estimation methods depend on the validity of the assumed model. In many cases, the model structure is related to physical information. We discuss a number of statistical selection methods to distinguish between two possible models using least-squares estimation and assuming a spherical head model. The first model has a single moving source whereas the second has two stationary sources; these may result in similar EEG/MEG measurements. The need to decide between such models occurs for example in Jacksonian seizures (e.g., epilepsy) or in intralobular activities, where a model with either two stationary dipole sources or a single moving dipole source may be possible. We also show that all of the selection methods discussed choose the correct model with probability one when the number of trials goes to infinity. Finally we present numerical examples and compare the performances of the methods by varying parameters such as the signal-to-noise ratio, source depth, and separation of sources, and also apply the methods to real MEG data for epilepsy.

Algorithms↗

PubMed2005

Line-source modeling and estimation with magnetoencephalography.

We propose a number of source models that are spatially distributed on a line for magnetoencephalography (MEG) using both a spherical head with radial sensors for more efficient computation and a realistic head model for more accurate results. We develop these models with increasing degrees of freedom, derive forward solutions, maximum-likelihood (ML) estimates, and Cramér-Rao bound (CRB) expressions for the unknown source parameters. A model selection method is applied to select the most appropriate model. We also present numerical examples to compare the performances and computational costs of the different models, to determine the regions where better estimates are possible and when it is possible to distinguish between line and focal sources. We demonstrate the usefulness of the proposed line-source models over the previously available focal source model in certain distributed source cases. Finally, we apply our methods to real MEG data, the N2O response after electric stimulation of the median nerve known to be an extended source.

Algorithms↗

PubMed2002

Optimization of orders in multichannel fractional Fourier-domain filtering circuits and its application to the synthesis of mutual-intensity distributions.

Owing to the nonlinear nature of the problem, the transform orders in fractional Fourier-domain filtering configurations have usually not been optimized but chosen uniformly. We discuss the optimization of these orders for multi-channel-filtering configurations by first finding the optimal filter coefficients for a larger number of uniformly chosen orders, and then maintaining the most important ones. The method is illustrated with the problem of synthesizing desired mutual-intensity distributions. The method we propose allows those fractional Fourier domains, which add little benefit to the filtering process but increase the overall cost, to be pruned, so that comparable performance can be attained with less cost, or higher performance can be obtained with the same cost. The method we propose is more likely to be useful when confronted with low-cost rather than high-performance applications, because larger improvements are obtained when the use of a smaller number of filters is desired.

Journal Article↗