Regularized state observers for source activity estimation

Abstract

The brain is a complex system and the activity inside can describe non-linear behaviors where the signals of the EEG which are taken from the scalp represent the mixture of the activity in each distributed source inside the brain. This activity can be represented by non-linear models and the inverse problem for source activity estimation can consider these models in the solutions. This paper presents the design of linear and nonlinear regularized observers for neural activity estimation, where the solutions involve a discrete physiologically-based non-linear model as spatio-temporal constraints. Furthermore, this document presents the estimation of the regularization hyper-parameters based on the application of a genetic algorithm over the Generalized Cross Validation cost function, which reduced the computational load. The aforementioned methods are compared with Multiple Sparse Priors (MSP) method of the state-of-the-art by using a simulated and real EEG signals.