The human brain is a dynamic networked system. Patients with partial epileptic seizures
have focal regions that periodically diverge from normal brain network dynamics during …

Epilepsy is the second most common neurological disorder, affecting 0.6–0.8% of the
world's population. In this neurological disorder, abnormal activity of the brain causes …

The ability of computational intelligence methods to predict epileptic seizures is evaluated in
long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy …

Seizure prediction based on analysis of electroencephalogram signals has generated
considerable research interests. A reliable seizure prediction algorithm with minimal …

Background Many investigations based on nonlinear methods have been carried out for the
research of seizure detection. However, some of these nonlinear measures cannot achieve …

Objectives The aim of this study is to develop a model based seizure prediction method.
Methods A neural mass model was used to simulate the macro-scale dynamics of …

In order to predict epileptic seizures many precursory features, extracted from the EEG
signals, have been introduced. Before checking out the performance of features in detection …

The aim of this paper is to develop an automated system for epileptic seizure prediction from
intracranial EEG signals based on Hilbert‐Huang transform (HHT) and Bayesian classifiers …

We investigate the relative merit of phase-based methods—mean phase coherence,
unweighted and weighted phase lag index—for estimating the strength of interactions …

Many motor imagery based BCI systems will utilize the common spatial pattern (CSP)
feature for task classification. However, the frequency band and time interval involved for …