There are several ways of recording psychophysiology data from humans, for example
Galvanic Skin Response (GSR), Electromyography (EMG), Electrocardiogram (ECG) and …

In order to improve the accuracy of emotional recognition by end-to-end automatic learning
of emotional features in spatial and temporal dimensions of electroencephalogram (EEG) …

The conventional emotion recognition methods are mostly based on the frequency
characteristics of electroencephalograph (EEG) signals. However, spatial features are …

Advances in signal processing and machine learning have expedited
electroencephalogram (EEG)-based emotion recognition research, and numerous EEG …

Determining the psychophysiological state of people has been a significant issue in many
fields, such as the adaptation of disabled people to social life. Recently, various …

Emotion recognition is a crucial application in human–computer interaction. It is usually
conducted using facial expressions as the main modality, which might not be reliable. In this …

Since EEG signal acquisition is non-invasive and portable, it is convenient to be used for
different applications. Recognizing emotions based on Brain-Computer Interface (BCI) is an …

Abstract Brain-Computer Interaction (BCI) system intelligence has become more dependent
on electroencephalogram (EEG)-based emotion recognition because of the numerous …

Automatic electroencephalogram (EEG) emotion recognition is a challenging component of
human–computer interaction (HCI). Inspired by the powerful feature learning ability of …

EEG-based emotion recognition is a challenging and active research area in affective
computing. We used three-dimensional (arousal, valence and dominance) model of emotion …