Neural Activity Estimation from EEG Using an Iterative Dynamic Inverse Problem Solution

Estimation of neural activity using Electroencephalography (EEG) signals allows identifying with high temporal resolution those brain structures related to pathological states. This work aims to improve spatial resolution of estimated neural activity employing time-varying dynamic constraints within the iterative inverse problem framework. Particularly, we introduce the use of Dynamic Neural Fields (DNF) to represent neural activity directly related to epileptic foci localization adequately. So, we develop a DNF-based time variant estimation model in the form of an Iterative Regularization Algorithm (IRA) that carries out neural activity estimation at every time EEG sample. The IRA model performance that is evaluated on simulated and real cases is compared with the baseline static and dynamic methods under several noise conditions. To this end, we use different error measures showing that the IRA estimation model can be more accurate and robust than the other compared methods.