Eduardo Giraldo

Estimation of dynamic neural activity using a Kalman filter approach based on physiological models

This paper presents a new method to estimate dynamic neural activity from EEG signals. The method is based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account. The filters performance (in terms of estimation error) is analyzed for the cases of linear and nonlinear models having either time invariant or time varying parameters. The best performance is achieved with a nonlinear model with time-varying parameters.

EEG source localization based on multivariate autoregressive models using Kalman filtering

The estimation of current distributions from electroencephalographic recordings poses an inverse problem, which can approximately be solved by including dynamical models as spatio-temporal constraints onto the solution. In this paper, we consider the electrocardiography source localization task, where a specific structure for the dynamical model of current distribution is directly obtained from the data by fitting multivariate autoregressive models to electroencephalographic time series. Whereas previous approaches consider an approximation of the internal connectivity of the sources, the proposed methodology takes into account a realistic structure of the model estimated from the data, such that it becomes possible to obtain improved inverse solutions. The performance of the new method is demonstrated by application to simulated electroencephalographic data over several signal to noise ratios, where the source localization task is evaluated by using the localization error and the data fit error. Finally, it is shown that estimating MVAR models makes possible to obtain inverse solutions of considerably improved quality, as compared to the usual instantaneous inverse solutions, even if the regularized inverse of Tikhonov is used.

Identification of Relevant Inter-channel EEG Connectivity Patterns: A Kernel-Based Supervised Approach

Extraction of brain patterns from electroencephalography signals to discriminate brain states has been an important research field to the develop of non-invasive applications like brain-computer-interface systems or diagnosis of neurodegenerative diseases. However, most of the state-of-the-art methodologies use observations derived from each electrode independently, without considering the possible dependencies between channels. To improve understanding of brain functionality, connectivity analysis have been developed. Nevertheless in those works, where connectivity measures are included, the total number of connections is high dimensional, and the relevance of connectivity values is not considered. To cope with this issue, we propose a kernel-based inter-channel connectivity relevance analysis (termed ConnRA), for such a purpose, linear dependencies between channel signals are extracted using coherence measures over specific sub-frequency bands, and a similarity criterion is implemented to rank the contribution of each channel-to-channel connection for a specific task. Experimental validation carried out on a database of brain-computer interfaces, demonstrate very promising results, making the proposed methodology a suitable alternative to support many neurophysiological applications.

Identificación robusta aplicada a un sistema de control de un puente grúa

This study presents a methodology to perform the robust identification of a gantry crane control system. The robust modeling was performed by three sequential Kalman filters, where two of them are the dual Kalman filter for estimating the parameter and state system, and third filter is the robust statistic Kalman filter. The obtained results show that the robust statistic Kalman filter presents responses smoother for the position and the velocity of the gantry crane, compared with the standard identification strategies.

Regularización de problemas dinámicos inversos en la generación EEG mediante estimación dual basada en el filtro de Kalman

This study presents the applications of two sequential Kalman filters to perform dynamic inverse problems regularization as the reconstruction of current distributions in neural activity in the brain, from electroencephalography signals. Kalman filter is an efficient algorithm for reconstructing of optimal way the current densities under some operation hypothesis, these are: the relationship between consecutives state; among a state and an observations, are both given by Gaussian distributions. The proposed methodology obtains consistent results with the state-of-the-art, when sources numbers rise; however, it needs a change in the estimation structure, since it can incur high computational cost.

Identificación de un generador de inducción doblemente alimentado basado en el filtro de Kalman en presencia de datos espurios

Este documento presenta una metodologia de identificacion de un generador de induccion doblemente alimentado(DFIG) en presencia de datos espurios. El DFIG es ampliamente usado en la produccion de energia electrica a partirdel viento; un problema en el control de estas maquinas, es el cambio en los parametros del sistema, haciendo queel esquema de control no tenga un optimo desempeno. Ademas, si el sistema sensorial no es confiable, se puedeincurrir en que las mediciones contengan datos espurios. Para llevar a cabo la identificacion se emplea tres filtros deKalman secuenciales, dos de ellos corresponden al filtro de Kalman dual, el otro corresponde al filtro de Kalman deestadistica robusta. La metodologia se implemento en Matlab, mostrando que la tecnica no se ve afectada por datosespurios, obteniendo errores residuales menores al 1.2% para la identificacion del DFIG en presencia de estos datos.

Estimación Dinámica Neuronal a partir de Señales Electroencefalográficas sobre un Modelo Realista de la Cabeza

In this paper is presented a method for neural activity estimation over the brain that take into account, for the solution of the inverse problem, a dynamic model for the neural activity in a realistic head model calculated with bounded elements method, according to a physiologically based model that describes the real interaction between neurons. The solution of the inverse problem is calculated using high performance computing. This analysis is performed for simulated EEG signals for SNR of 25 dB, 15 dB and 5 dB. The obtained results show the robustness of the estimation method that includes the dynamic model in comparison with the static model for several levels of noise.