Electroencephalography (EEG) is a complicated, non-stationary signal that requires
extensive preprocessing and feature extraction approaches to be accurately analyzed. In …

In this paper, we propose an end-to-end epilepsy seizure prediction method based on multi-
layer perceptrons (MLPs). The proposed method mainly contains two functional blocks: the …

The intelligent recognition of electroencephalogram (EEG) signals is a valuable tool for
epileptic seizure classification. Given that visual inspection of EEG signals is time …

Accurate early prediction of epileptic seizures can provide timely treatment for patients.
Previous studies have mainly focused on a single temporal or spatial dimension, making it …

Changes in the frequency composition of the human electroencephalogram are associated
with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of …

A clinical condition known as epilepsy occurs when the brain's regular electrical activity is
disturbed, resulting in a rapid, aberrant, and excessive discharge of brain neurons. The …

Epilepsy, one of the most common neurological diseases in China, is notorious for its
spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among …

Seizure prediction using EEG has significant implications for the daily monitoring and
treatment of epilepsy patients. However, the task is challenging due to the underlying …

Purpose: Approximately 1% of the world population is currently suffering from epilepsy.
Successful seizure prediction is necessary for those patients. Influenced by neurons in their …

Epilepsy, one of the world's most common neurological diseases, impacts over 1% of the
global population. Accurate early prediction of epileptic seizure has a great influence on …