Abstract Background and Objective Sleep was highly imperative in human daily life.
However, an increasing number of people were undergoing sleep deprivation and sleep …

Automatic epilepsy detection based on electroencephalography (EEG) is crucial for
advancing the diagnosis and treatment of epilepsy. In this paper, we propose a novel …

Although recent studies have proposed seizure detection algorithms with good sensitivity
performance, there is a remained challenge that they were hard to achieve significantly short …

Classifying individuals with neurological disorders and healthy subjects using EEG is a
crucial area of research. The current feature extraction approach focuses on the frequency …

Epilepsy is a neurological condition characterized by temporary disruptions in the brain's
electrical activity. This disorder can significantly impact the quality of life for those affected …

Epilepsy, a prevalent neurological disorder, typically requires a complex diagnostic process
involving medical history inquiry, physical examination, head computed tomography, and …

Schizophrenia (SZ) is a serious mental disorder that can mainly be distinguished by
symptoms including delusions and hallucinations. This mental disorder makes difficult …

The automation of sleep stage classification from polysomnography (PSG) signals is a
critical task in sleep research, traditionally reliant on the manual extraction of features …

Pre-processing and feature extraction are essential steps for EEG signal classification based
on Machine Learning. An appropriate choice of signal processing methodology in these two …

Epilepsy, a chronic brain disease with recurrent attacks, is the most common neurological
disease in humans. It is extremely important for patients with epilepsy to accurately and …