Brain–computer interfaces (BCIs) have attracted much attention recently, triggered by new
scientific progress in understanding brain function and by impressive applications. The aim …

Epilepsy is a chronic disorder of the central nervous system that predisposes individuals to
experiencing recurrent seizures. It affects 3 million Americans and 50 million people world …

OBJECTIVE: Research in seizure prediction from intracranial EEG has highlighted the
usefulness of bivariate measures of brainwave synchronization. Spatio-temporal bivariate …

Epilepsy is a malfunction of the brain that affects over 50 million people worldwide. Epileptic
seizures are usually characterized by an abnormal synchronized firing of neurons involved …

In the past, considerable effort has been devoted to the development of signal processing
techniques aimed at characterizing brain connectivity from signals recorded from spatially …

Different types of analyses of scalp and intracranial electroencephalography (EEG)
recordings using linear and nonlinear time series analysis method have been done. They …

Previous studies with intracranial recordings suggested that a more random spatial structure
of functional brain networks could be related to seizure generation. Here, we studied …

Networks—collections of interacting elements or nodes—abound in the natural and
manmade worlds. For many networks, complex spatiotemporal dynamics stem from patterns …

In this paper, we introduce a modification of a mean-field model used to describe the brain's
electrical activity as recorded via electroencephalography (EEG). The focus of the present …

This paper presents a new methodology for automatically learning an optimal
neurostimulation strategy for the treatment of epilepsy. The technical challenge is to …