Among various physiological signal acquisition methods for the study of the human brain,
EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive …

Wavelet family and differential evolution are proposed for categorization of epilepsy cases
based on electroencephalogram (EEG) signals. Discrete wavelet transform is widely used in …

Recently, industrial sectors that stage occupational and environment safety critical tasks,
such as the oil and gas industry, have been interested in monitoring biological parameters to …

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 …

Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep
arousals are associated with symptoms such as sympathetic activation, non-restorative …

Electroencephalograph (EEG) is supposed to be a major challenge in the area of
biomedical signal processing. Being one of the widely used invasive techniques, it is …

Humans experience a variety of emotions throughout the course of their daily lives, including
happiness, sadness, and rage. As a result, an effective emotion identification system is …

Detection of sleep spindles, a special type of burst brainwaves recordable with
electroencephalography (EEG), is critical in examining sleep-related brain functions from …

Among electroencephalogram (EEG) signal emotion recognition methods based on deep
learning, most methods have difficulty in using a high-quality model due to the low resolution …

Background The classification of sleep signals is a subjective and time consuming task. A
large number of automatic classifiers have been published in the past decade but a sleep …