Identificación Estable de Fuentes Asociadas a Focos Epilépticos Ubicadas sobre la Corteza / Stable Identification of Sources Associated with Epileptic Focus on the Cerebral Cortex

Resumen Objetivo: Presentar un algoritmo estable que determina, a partir de mediciones electroencefalográficas, los parámetros de fuentes de tipo dipolar asociadas a focos epilépticos ubicados sobre la superficie de la corteza cerebral. Metodología: Se utiliza un problema de contorno para establecer correlaciones entre la fuente y la medición. El problema se divide en dos subproblemas lineales y en cada uno de ellos, se utilizan el método de mínimos cuadrados y la regularización de Tikhonov para encontrar soluciones estables. Estos subproblemas son problemas mal planteados en el sentido de Hadamard, debido a la inestabilidad numérica que presentan, es decir, pequeños cambios en las mediciones pueden producir grandes variaciones en la solución de cada problema. El parámetro de regularización de Tikhonov fue elegido usando el método de la curva L. Para hallar la solución del problema de contorno se utiliza el método de las series de Fourier y el Método del Elemento Finito. Resultados: Se propuso un tipo de fuente para representar a los focos epilépticos en la corteza cerebral y un algoritmo estable para el problema de identificación de los parámetros de dichas fuentes. Se desarrollaron ejemplos sintéticos y programas en MATLAB para el caso de geometría simple bidimensional. Originalidad: La separación del problema original en dos subproblemas así como los ejemplos sintéticos son producto de esta investigación. Conclusión general: Se propuso un algoritmo estable que determina a los parámetros de fuentes de corriente dipolar definidas en la corteza cerebral.

Abstract Objective: To present a stable algorithm that determines, from electroencephalographic measurements, the parameters of dipolar sources associated with epileptic foci located on the cerebral cortex. Methodology: A boundary value problem is used to establish correlations between the sources and the measurements. The problem is divided into two linear subproblems and in each one, the method of Minimum Square and the Tikhonov regularization are used for finding stables solutions. These subproblems are an ill-posed problem in the Hadamard sense, which is due to the numerical instability, that is, small changes in the data can produce substantial variations in the solution of each problem. The Tikhonov regularization parameter was chosen using the L curve method. To find the solution of the boundary value problem are used the Fourier series method and the Finite Element Method. Results: A type of source that represents the epileptic foci on the cerebral cortex and a stable algorithm for finding the parameter of these sources were proposed. Synthetics examples and MATLAB programs were developed for the case of bidimensional geometry. Originality: The separation of the original problem into two subproblems and the synthetics examples are a product of this research. Conclusion: A stable algorithm was proposed for determining the parameters of the dipolar current defined on the cerebral cortex.

Prototipo de Silla de Ruedas Dirigida Usando Parpadeos / A Wheel Chair Prototype Moved by means of Eye Blinks

Resumen El presente trabajo describe un prototipo de una silla de ruedas que es dirigido hacia enfrente y hacia atrás usando 2 o 3 parpadeos, respectivamente, y es detenido cuando se alcanzan ciertos niveles de atención. El objetivo principal es que las personas que tienen discapacidad motora en sus extremidades puedan usarlo para desplazarse y les brinde autonomía. Para captar la señal de los parpadeos, se utilizó la diadema MindWave Mobile de Neurosky. Se implementó un circuito electrónico en conjunto con Arduino que permite complementar la ejecución del accionamiento del prototipo. El prototipo se probó con 10 personas cuyas edades oscilan entre 20 y 35 años. Los resultados muestran que, en un 80% de los casos, el prototipo se mueve correctamente. La gran ventaja del presente trabajo es que la interfaz cerebro-computadora con la que cuenta este prototipo no requiere entrenamiento previo del sistema, por lo cual, puede ser usado por cualquier persona. Además, su costo es más accesible comparado con otros dispositivos para el mismo fin.

Abstract The present work describes a prototype of a wheel chair directed by means of eye blinks, which can be moved forwards, and backwards using 2 or 3 eye blinks, respectively, and stopped when a certain attention level is met. The main objective of this work is to help people, who have motor disabilities on their arms and legs, move and have autonomy. In order to register the eye blinking signals, the MindWave Mobile device from Neurosky was used. Moreover, an electronic circuit in combination with Arduino has been used to make the prototype work. This prototype has been tested in 10 healthy people from 20 to 35 years old. According to the results, in 80% of the cases the prototype worked correctly. The main advantage of the present work is that the brain-computer interface, which is part of the prototype, does not require training, and hence, it could be used by most of the people. Moreover, its cost is less than similar devices.

Introducing a Combination of ICA-EMD to Suppress Muscle and Ocular Artifacts in EEG Signals.

This paper presents a combination of Independent Component Analysis (ICA) with Empirical Mode Decomposition (EMD) to suppress muscle and ocular artifacts in electroencephalographic (EEG) signals: By means of ICA, the EEG signals are decomposed into independent components. To avoid the suppression of artifactual components still containing physiological information, EMD is applied to decompose the components in Intrinsic Mode Functions (IMFs). The IMFs with mainly muscle artifacts are removed, and a new data set of independent components without muscle artifacts is generated. From this set, the components containing ocular artifacts are suppressed and clean data are reconstructed. In this way, the muscle and ocular artifacts are better suppressed than using pure ICA, or pure EMD. The performance of the proposed combination is applied to a semi-simulated data set, and three real EEG data sets from healthy subjects contaminated with both artifacts.

Validating the effect of muscle artifact suppression in localizing focal epilepsy.

Source localization of an epileptic seizure is becoming an important diagnostic tool in pre-surgical evaluation of epileptic patients. However, for localizing the epileptogenic zone precisely, the epileptic activity needs to be isolated from other activities that are not related to the epileptic source. In this study, we aim at an investigation of the effect of muscle artifact suppression by using a low-pass filter (LPF), independent component analysis (ICA), and a combination of ICA-LPF prior to source localization in focal epilepsy. These techniques were applied on the EEG data obtained from a left-temporal lobe epileptic patient by artificially contaminating the isolated spike interval, present in the four left-temporal electrodes, with a muscle artifact. The results show that the muscle artifact was fully suppressed. Applying the dipole and current-density reconstruction (CDR) source-analysis algorithms on the filtered data, we were able to identify the location of the epileptogenic zone similar to that of the original undistorted data.