Identification of sources of seizures in the brain is of paramount importance, particularly for drug-resistant epilepsy patients who may require surgical operation. Interictal epileptiform discharges (IEDs), which may or may not be frequent, are known to originate from seizure networks. Delayed responses (DRs) to brain electrical stimulation have been recently discovered. If DRs and IEDs come from the same location and the DRs can be accurately localized, there will be a significant step in the identification of the source of seizures. The solution to this important question has been investigated in this paper. For this, we have exploited the morphology of these spike-type, events as well as the variability in their temporal location, to develop new constraints for an adaptive Bayesian beamformer that outperforms the conventional and recently proposed beamformers. This beamformer is applied to an array (a.k.a mat) of cortical EEG electrodes. As the significant outcome of applying this beamformer, it is very likely (if not certain) that the IEDs and DRs for an epileptic subject originate from the same location in the brain. This paves the way for a quick identification of the source(s) of seizure in the brain.

A generative adversarial network (GAN) enables mapping a data sample from one domain to another one. It has extensively been employed in image-to-image and text-toimage translation. We propose an EEG-to-EEG translation model based on a GAN structure in which a conditional GAN (cGAN) is combined with a variational autoencoder (VAE), named as VAE-cGAN. Scalp EEG (sEEG) is plagued by noise and suffers from low resolution. On the other hand, intracranial EEG (iEEG) recordings enjoy high resolution. Here, we consider the task of mapping the sEEG to iEEG to enhance the sEEG resolution.