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.