Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …

Various automated/semi-automated medical diagnosis systems based on human physiology
have been gaining enormous popularity and importance in recent years. Physiological …

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a
user, which is considered an important factor in human-machine-interaction. Many methods …

Traditional machine learning methods suffer from severe overfitting in EEG-based emotion
reading. In this paper, we use hierarchical convolutional neural network (HCNN) to classify …

This paper introduces a method for feature extraction and emotion recognition based on
empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into …

Electrodermal activity (EDA) is indicative of psychological processes related to human
cognition and emotions. Previous research has studied many methods for extracting EDA …

In this research, emotional states in arousal/valence dimensions have been classified using
minimum number of channels and frequency bands of EEG signal. Using the discrete …

Stress is inevitably experienced by almost every person at some stage of their life. A reliable
and accurate measurement of stress can give an estimate of an individual's stress burden. It …

The objective of this paper is online recognition of human emotions based on
electroencephalogram (EEG) signals. The emotions are originated from the central and …

Stress is a major growing concern in our day and age adversely impacting both individuals
and society. Stress research has a wide range of benefits from improving personal …