Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

Electroencephalography (EEG) has been a staple method for identifying certain health
conditions in patients since its discovery. Due to the many different types of classifiers …

The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …

This study aims to evaluate the application of the wavelet transform (WT) as a pre-
processing and hybridization technique for Recurrent Neural Networks (RNN). The …

This study investigates the properties of the brain electrical activity from different recording
regions and physiological states for seizure detection. Neurophysiologists will find the work …

Objective: Electroencephalography (EEG) has an influential role in neuroscience and
commercial applications. Most of the tools available for EEG signal analysis use machine …

Scientists and researchers have tried to establish a bond between the emotions conveyed
and the subsequent mood perceived in a person. Emotions play a major role in terms of our …

In this paper, the estimation of the remaining useful life (RUL) of angular contact ball bearing
using time-domain signal processing method is discussed. An experimental setup based on …

Epilepsy is a chronic neurological disorder with a comparatively high prevalence rate. It is a
condition characterized by repeated and unprovoked seizures. Seizures are managed with …

The innovation of electroencephalography (EEG) more than a century ago supports the
technique to assess brain structure and function in clinical health and research applications …