Emotion recognition is a challenging problem in Brain‐Computer Interaction (BCI).
Electroencephalogram (EEG) gives unique information about brain activities that are created …

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph
(EEG) processing techniques to recognize emotion states is proposed in this paper. Three …

Emotion recognition from Electroencephalography (EEG) is proved to be a good choice as it
cannot be mimicked like speech signals or facial expressions. EEG signals of emotions are …

Electroencephalogram (EEG) monitors the brain's electrical activity and carries useful
information regarding the subject's emotional states. Due to the nonstationary and being …

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 …

Understanding learners' emotions can help optimize instruction sand further conduct
effective learning interventions. Most existing studies on student emotion recognition are …

Automatic emotion recognition is one of the most challenging tasks. To detect emotion from
nonstationary EEG signals, a sophisticated learning algorithm that can represent high‐level …

The electroencephalogram (EEG) has great attraction in emotion recognition studies due to
its resistance to deceptive actions of humans. This is one of the most significant advantages …

Recently, the availability of large EEG datasets, advancements in Brain-Computer interface
(BCI) systems and Machine Learning have led to the implementation of deep learning …

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 …