Estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role in
developing robust Brain-Computer Interface (BCI) systems. In our research, we used Deep …

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

Emotion is the most important component in daily interaction between people. Nowadays, it
is important to make the computers understand user's emotion who interacts with it in human …

The temporal and spatial information of electroencephalogram (EEG) are essential for the
emotion recognition model to learn the discriminative features. Hence, we propose a novel …

Introduction: The traditional machine learning-based emotion recognition models have
shown effective performance for classifying Electroencephalography (EEG) based emotions …

Emotion is a significant parameter in daily life and is considered an important factor for
human interactions. The human-machine interactions and their advanced stages like …

Emotion recognition is a vital but challenging step in creating passive brain-computer
interface applications. In recent years, many studies on electroencephalogram (EEG)-based …

Currently, Fourier-based, wavelet-based, and Hilbert-based time–frequency techniques
have generated considerable interest in classification studies for emotion recognition in …

Many scientific studies have been concerned with building an automatic system to recognize
emotions, and building such systems usually relies on brain signals. These studies have …

Emotion recognition using electroencephalography (EEG) is becoming an interesting topic
among researchers. It has made a remarkable entry in the domain of biomedical, smart …