The benefits of early detection and classification of epileptic seizures in analysis, monitoring
and diagnosis for the realization and actualization of computer-aided devices and recent …

Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …

In this paper, we propose and evaluate Epilepsy-Net, a collection of deep learning EEG
signal processing tools to detect epileptic seizures against non-epileptic seizures without …

Abstract Brain-Computer Interfaces (BCI) is an exciting and emerging research area for
researchers and scientists. It is a suitable combination of software and hardware to operate …

Purpose Depression is a global challenge causing psychological and intellectual problems
that require efficient diagnosis. Electroencephalogram (EEG) signals represent the …

One of the most critical processes is feature selection, which eliminates features that may
decrease classification performance and increase computational time. In this paper, we …

Epilepsy affects millions of people worldwide every year and remains an open subject for
research. Current development on this field has focused on obtaining computational models …

Despite over a decade of promising results, closed-loop neurostimulation for the treatment of
drugresistant epilepsy (DRE) still relies on manual parameter tuning and yields …

Epilepsy is a brain disease in nerves which causes sudden seizure, sensations, and once in
a while loss of mindfulness. This disorder is difficult to find manually because of its …

This paper provides a review of machine learning-based approaches to
electroencephalogram (EEG) data classification. Machine learning algorithms are used to …