With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Recent advancements in machine learning and deep learning (DL) based neural decoders
have significantly improved decoding capabilities using scalp electroencephalography …

Introduction Epilepsy is a global chronic disease that brings pain and inconvenience to
patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that …

Epilepsy is a neurological disorder caused by abnormal brain discharges, which can be
diagnosed by electroencephalography (EEG). Although EEG signals are usually easy to …

Epilepsy is a chronic brain disease, and identifying seizures based on
electroencephalogram (EEG) signals would be conducive to implement interventions to help …

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for
epileptic electroencephalogram (EEG) signal classification. The goal is to develop a …

Cross-patient automatic epileptic seizure detection through electroencephalogram (EEG) is
significant for clinical application and research. However, most automatic seizure detection …

Epilepsy is one of the most common neurological diseases. Clinically, epileptic seizure
detection is usually performed by analyzing electroencephalography (EEG) signals. At …

High-quality brain signal data recorded by Stereoelectroencephalography (SEEG)
electrodes provide clinicians with clear guidance for presurgical assessments for epilepsy …

Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis
of several brain disorders, including Alzheimer's disease and epilepsy. Until recently …