6 research outputs found
Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network
Electromagnetic source imaging (ESI) requires solving a highly ill-posed
inverse problem. To seek a unique solution, traditional ESI methods impose
various forms of priors that may not accurately reflect the actual source
properties, which may hinder their broad applications. To overcome this
limitation, in this paper a novel data-synthesized spatio-temporally
convolutional encoder-decoder network method termed DST-CedNet is proposed for
ESI. DST-CedNet recasts ESI as a machine learning problem, where discriminative
learning and latent-space representations are integrated in a convolutional
encoder-decoder network (CedNet) to learn a robust mapping from the measured
electroencephalography/magnetoencephalography (E/MEG) signals to the brain
activity. In particular, by incorporating prior knowledge regarding dynamical
brain activities, a novel data synthesis strategy is devised to generate
large-scale samples for effectively training CedNet. This stands in contrast to
traditional ESI methods where the prior information is often enforced via
constraints primarily aimed for mathematical convenience. Extensive numerical
experiments as well as analysis of a real MEG and Epilepsy EEG dataset
demonstrate that DST-CedNet outperforms several state-of-the-art ESI methods in
robustly estimating source signals under a variety of source configurations.Comment: 15 pages, 14 figures, and journa
EEG source localization analysis in epileptic children during a visual working-memory task
We localize the sources of brain activity of children with epilepsy based on
EEG recordings acquired during a visual discrimination working memory task. For
the numerical solution of the inverse problem, with the aid of age-specific MRI
scans processed from a publicly available database, we use and compare three
regularization numerical methods, namely the standarized Low Resolution
Electromagnetic Tomography (sLORETA), the weighted Minimum Norm Estimation
(wMNE) and the dynamic Statistical Parametric Mapping (dSPM). We show that all
three methods provide the same spatio-temporal patterns of differences between
epileptic and control children. In particular, our analysis reveals
statistically significant differences between the two groups in regions of the
Parietal Cortex indicating that these may serve as "biomarkers" for diagnostic
purposes and ultimately localized treatment
Dynamic inverse problem solution considering non-homogeneous source distribution with frequency spatio temporal constraints applied to brain activity reconstruction
Para reconstruir la actividad cerebral es necesario estimular la ubicación de las fuentes activas del cerebro. El método de localización de fuentes usando electroencefalogramas es usado para esta tarea por su alta resolución temporal. Este método de resolver un problema inverso mal planteado, el cual no tiene una solución única debido al que el números de variables desconocidas es mas grande que el numero de variables conocidas. por lo tanto el método presenta una baja resolución espacial..
Non linear time varying model identification in ill-posed problems corresponding to neural activity estimation from EEG signals
Esta tesis trata el problema inverso dinámico para la reconstrucción de fuentes a partir de señales EEG usando dos métodos: solución del Problema Inverso Dinámico considerando Restricciones Variantes e Invariantes con el Tiempo, y solución del Problema Inverso Dinámico Ponderado. Los métodos discutidos comprenden principalmente dos contribuciones: En primer lugar, la introducción de un modelo discreto no lineal basado en consideraciones fisiológicas que describa adecuadamente la dinámica de la actividad neuronal. En segundo lugar, la estimación de parámetros variantes en el tiempo que permitan mejorar el modelo no lineal, haciéndolo apropiado para la localización de fuentes electroencefalográficas durante actividad normal y patológica, tal como ataques epilépticos. La estimación realizada usando los modelos no lineales propuestos, presenta mejores resultados en términos del error de reconstrucción, comparado con métodos lineales o invariantes con el tiempoAbstract : This thesis addresses the dynamical inverse problem of EEG source reconstruction by using two main approaches: Dynamic Inverse Problem solution considering Time Varying and Time invariant Constraints, and Weighted Dynamic Inverse Problem solution. Discussed approach of representation comprises two main contributions: Firstly, the introduction of a discrete–time nonlinear model grounded on physiological considerations that explains better the dynamics of the brain neural activity. Secondly, the inclusion of estimation of time varying parameters that allows the enhancement of the nonlinear model, making it suitable for electroencephalographic source localization of such abnormal neuronal activity as epileptic seizures. The estimation that is performed using proposed nonlinear dynamic models with time varying parameters provides an improvement in terms of reconstruction error, if comparing with similar referred linear approximationsDoctorad