Epilepsy is among the most dynamic disorders in neurology. A canonical view holds that
seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans …

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that
is often approached using neurophysiological signals as the basis for building a reliable …

Affective Computing has emerged as an important field of study that aims to develop
systems that can automatically recognize emotions. Up to the present, elicitation has been …

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …

Deep Convolutional Neural Network (CNN) has achieved remarkable results in computer
vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn …

Epileptogenic networks are defined by the brain regions involved in the production and
propagation of epileptic activities. In this review we describe the historical, methodologic …

Nowadays, depression is the world's major health concern and economic burden worldwide.
However, due to the limitations of current methods for depression diagnosis, a pervasive …

Background Seizure prediction would be clinically useful in patients with epilepsy and could
improve safety, increase independence, and allow acute treatment. We did a multicentre …

Brain connectivity can be modeled and quantified with a large number of techniques. The
main objective of this paper is to present the most modern and widely established …

We compare dynamical properties of brain electrical activity from different recording regions
and from different physiological and pathological brain states. Using the nonlinear prediction …