A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …

Brain signals refer to the biometric information collected from the human brain. The research
on brain signals aims to discover the underlying neurological or physical status of the …

Deep Convolutional Neural Network (CNN) has achieved remarkable results in computer
vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn …

The recent advances in pervasive sensing technologies have enabled us to monitor and
analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to …

Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world
by decoding individuals' brain signals into commands recognizable by computer devices …

Electroencephalogram (EEG) signals, generated during the neuron firing, are an effective
way of predicting such seizure and it is used widely in recent days for classifying and …

Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …

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 …

The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of
epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased …

Objective Visual assessment of the EEG still outperforms current computer algorithms in
detecting epileptiform discharges. Deep learning is a promising novel approach, being able …