Emotion recognition (ER) from Electroencephalogram (EEG) signals is a challenging task
due to the non-linearity and non-stationarity nature of EEG signals. Existing feature …

Featured Application The method presented in this study can be applied in many fields, such
as mental health care, entertainment consumption behavior, society safety, and so on. For …

Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as
physical, behavioral, and mental changes. In the latest days, physiological activity has been …

Much attention has been paid to the recognition of human emotions with the help of
electroencephalogram (EEG) signals based on machine learning technology. Recognizing …

EEG signal has been widely used in emotion recognition. However, too many channels and
extracted features are used in the current EEG-based emotion recognition methods, which …

The purpose of this research is to use a cross-dataset approach to construct an EEG-based
emotion recognition system. So far, numerous modeling strategies for emotion recognition …

The emotional state of people plays a key role in physiological and behavioral human
interaction. Emotional state analysis entails many fields such as neuroscience, cognitive …

Deep learning (DL) technologies have recently shown great potential in emotion recognition
based on electroencephalography (EEG). However, existing DL-based EEG emotion …

Real-time emotion recognition has been an active field of research over the past several
decades. This work aims to classify physically disabled people (deaf, dumb, and bedridden) …

In the field of human-computer interaction, automatic emotion recognition is an important
and challenging task. As a physiological signal that directly reflects the brain activity, EEG …