Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches

Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient

A new method for detection and source analysis of EEG spikes

In the past our research group has developed a method for the detection of focal epileptic EEG (electroencephalogram) spikes that is based on the dipole source localization technique and provides a source localization for each detected spike. In this paper we revisit this method and propose a more accurate explanation of its behavior. Based on this we (i) propose a new method for the detection of epileptic EEG spikes in which the eccentricity of the fitted dipole serves as a new decision variable (ii) conclude that for EEG spike detection one has to make a distinction between EEGs acquired during sleep and during wake