Early detection and proper treatment of epilepsy is essential and meaningful to those who
suffer from this disease. The adoption of deep learning (DL) techniques for automated …

The burden of neurological disorders is huge on global health and recognized as major
causes of death and disability worldwide. There are more than 600 neurological diseases …

The interpatient variability still poses a great challenge for the real-world application of
electroencephalogram (EEG)-based seizure prediction, where most previous methods could …

Power spectrum analysis is one of the effective tools for classifying epileptic signals based
on electroencephalography (EEG) recordings. However, the conflation of periodic and …

Mild Cognitive Impairment (MCI) is often considered a precursor to Alzheimer's disease
(AD), with a high likelihood of progression. Accurate and timely diagnosis of MCI is essential …

When building seizure prediction systems, the typical research scenario is patient-specific.
In this scenario, the model is limited to performing well for individual patients and cannot …

Automatic seizure detection using electroen-cephalogram (EEG) can significantly expedite
the diagnosis of epilepsy, thereby facilitating prompt treatment and reducing the risk of future …

Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their
quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of …

Although deep neural networks have shown promise in the patient-specific context closely
related to training and testing data distributions, they face challenges when applied to real …

Machine learning algorithms for seizure detection have shown great diagnostic potential,
with recent reported accuracies reaching 100%. However, few published algorithms have …