In this paper we consider the dynamical inverse problem of EEG generation where a specific dynamics for the electrical current distribution is assumed. By casting this problem into a state space representation and assuming a specific class of parametric models for the dynamics, we can impose general spatio-temporal constraints onto the solution. For the purpose of estimating the parameters and evaluating the model, we employ the Akaike Bayesian Information Criterion (ABIC), which is based on the type II likelihood. As a new approach for estimating the current distribution we introduce a method which we call "Dynamic LORETA". A recursive penalized least squares (RPLS) step forms the main element of our implementation. Whereas LORETA exploits exclusively spatial information, Dynamic LORETA exploits both spatial and temporal information, such that it becomes possible to obtain improved i nverse solutions. The performance of the new method is evaluated by application to simulated EEG data, and a considerable improvement over LORETA is found. We also show results for the application to clinical EEG data.

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