Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as
engineering applications. However, one of the challenges associated with recording EEG …

This review systematically explores the application of transformer-based models in EEG
signal processing and brain-computer interface (BCI) development, with a distinct focus on …

Epilepsy is a neurological disorder of brain which is characterized by recurrent disorders.
And people with epilepsy and their families frequently suffer from stigma and discrimination …

Electroencephalography (EEG) is one of the most promising methods in the field of Brain-
Computer Interfaces (BCIs) due to its rich time-domain resolution and the availability of …

With the advances in pervasive sensor technologies, physiological signals can be captured
continuously to prevent the serious outcomes caused by epilepsy. Detection of epileptic …

In electroencephalography (EEG)-based emotion recognition systems, the distribution
between the training samples and the testing samples may be mismatched if they are …

Electroencephalography (EEG) is a commonly used clinical approach for the diagnosis of
epilepsy which is a life-threatening neurological disorder. Many algorithms have been …

Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis
of epilepsy and studying the human brain electrical activity. Previously, the traditional …

The purpose of this paper is to determine whether gamma-band activity detection is
improved when a filter, based on empirical mode decomposition (EMD), is added to the pre …

Electroencephalography (EEG) signals have been widely used to diagnose brain diseases
for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine …