Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …

A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …

Electroencephalography (EEG), as a non-invasive modality, enables the representation of
the underlying neuronal activities as electrical signals with high temporal resolution. In …

In this study, three popular signal processing techniques (Empirical Mode Decomposition,
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …

Electroencephalogram (EEG) comprises valuable details related to the different
physiological state of the brain. In this paper, a framework is offered for detecting the …

Epileptic seizure detection is commonly implemented by expert clinicians with visual
observation of electroencephalography (EEG) signals, which tends to be time consuming …

Motor imagery-based brain–computer interaction (MI-BCI) converts human neural activity
into computational information, often used as commands, by recognizing …

This paper describes the analysis of a deep neural network for the classification of epileptic
EEG signals. The deep learning architecture is made up of two convolutional layers for …

In this paper, a comprehensive analysis of focal and non-focal electroencephalography is
carried out in the empirical mode decomposition and discrete wavelet transform domains. A …

Epilepsy is a brain disorder characterized by sudden seizures, periodic abnormal and
inappropriate behaviour, and an altered state of consciousness. The visual diagnosis of …