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In this study a novel method for tracking and separation of event-related potential (ERP) subcomponents from trial to trial is considered. The sources of ERP subcomponents are assumed to be electric current dipoles (ECD). The shape of each ERP subcomponent is also supposed to be monophasic wave and modeled using a Gaussian waveform. We are interested in the estimation and tracking of ERP subcomponent...
A new algorithm is developed here for blind extraction of periodic signals. It is assumed that the fundamental frequencies of the sources (or alternatively one of the harmonics for each source) are known a priori. Necessary and sufficient conditions for blind source extraction of cyclostationary signals are introduced and the optimization problem is solved using steepest descent method for complex...
Recently we have developed a method for electroencephalogram (EEG) dipole source localization based on particle filtering (PF). In this study the method is combined with beamforming to eliminate the noise which is spatially uncorrelated with the desired signal and accordingly to improve its performance. The proposed beamforming is an optimum, linear and data independent filter which can be applied...
In this summary a method based on sequential Monte Carlo (SMC) techniques for EEG dipole source localization and tracking, in which a real head model is taken into account, is presented. The localization problem is formulated in the state space and the SMC method, which is a recursive non-Gaussian Bayesian solution, is employed. The method was applied to simulated and real data to show its potential...
In this paper a method based on wavelet transform (WT) and particle filtering (PF) for estimation of single trial event-related potentials (ERPs) is presented. The method is based on recursive Bayesian mean square estimation of wavelet coefficients of the ERPs, using PF as the estimator. Simulation results are provided to demonstrate the superior performance of PF over Kalman filtering (KF) for non-Gaussian...
In this paper an approach for solving the problem of event-related potential (ERP) identification, based on Kalman filtering and Kalman smoothing is presented. We assume that previous trials contain prior information relevant to the next trial and there are little dynamical changes from trial to trial. The results are presented for both simulated and real data. Simulated data were obtained by adding...
The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters,...
In this paper a new approach to accurately classify ECG arrhythmias through a combination of the wavelet transform and artificial neural network is presented. Three kinds of features in a very computationally efficient manner are computed as follows: 1-joint Time-Frequency features (discrete wavelet transform coefficients). 2-time domain features (R-R intervals). 3-Statistical feature (Form Factor)...
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