Cycles in epilepsy
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 …
seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans …
A review of EEG signal features and their application in driver drowsiness detection systems
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 …
is often approached using neurophysiological signals as the basis for building a reliable …
Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors
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 …
systems that can automatically recognize emotions. Up to the present, elicitation has been …
Machine learning for predicting epileptic seizures using EEG signals: A review
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …
researchers are striving towards employing these techniques for advancing clinical practice …
Applying deep learning for epilepsy seizure detection and brain map** visualization
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 …
vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn …
Defining epileptogenic networks: contribution of SEEG and signal analysis
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 …
propagation of epileptic activities. In this review we describe the historical, methodologic …
A pervasive approach to EEG‐based depression detection
H Cai, J Han, Y Chen, X Sha, Z Wang, B Hu… - …, 2018 - Wiley Online Library
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 …
However, due to the limitations of current methods for depression diagnosis, a pervasive …
Hippocampal network activity forecasts epileptic seizures
Seizures in people with epilepsy were long thought to occur at random, but recent methods
for seizure forecasting enable estimation of the likelihood of seizure occurrence over short …
for seizure forecasting enable estimation of the likelihood of seizure occurrence over short …
Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study
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 …
improve safety, increase independence, and allow acute treatment. We did a multicentre …
Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state
We compare dynamical properties of brain electrical activity from different recording regions
and from different physiological and pathological brain states. Using the nonlinear prediction …
and from different physiological and pathological brain states. Using the nonlinear prediction …