Electroencephalography (EEG) sensors are flexible and non-invasive sensoring devices for
the measurement of electrical brain activity which is extensively used in some areas of …

Epilepsy stands out as one of the common neurological diseases. The neural activity of the
brain is observed using electroencephalography (EEG), which allows the diagnosis of …

Graph theory analysis, as a mathematical tool, has been widely employed in studying the
connectivity of the brain to explore the structural organization. Through the computation of …

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 …

Electroencephalography (EEG) plays a crucial role in epilepsy analysis, and epileptic
seizure prediction has significant value for clinical treatment of epilepsy. Currently …

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as
emotion recognition, motor imagery and neurological diseases and disorders. A wide range …

Objective. Epilepsy is one of the most common neurological disorders and can have a
devastating effect on a person's quality of life. As such, the search for markers which indicate …

Seizure prediction can improve the quality of life for patients with drug-resistant epilepsy.
With the rapid development of deep learning, lots of seizure prediction methods have been …

Objective Epilepsy is the second most common brain neurological disease after stroke,
which has the characteristics of sudden and recurrence. Seizure prediction is seriously …

Epilepsy is a recurrent chronic brain disease that affects nearly 75 million people around the
world. Therefore, the ability to reliably predict epileptic seizures would be instrumental for …