Most past research on emotion recognition has used photographs of posed expressions
intended to depict the apex of the emotional display. Although these studies have provided …

Facial expressions of emotion involve a physical component of morphological changes in a
face and an affective component conveying information about the expresser's internal …

In recent years, deep learning (DL) techniques, and in particular convolutional neural
networks (CNNs), have shown great potential in electroencephalograph (EEG)-based …

Emotion recognition using brain wave signals involves using high dimensional
electroencephalogram (EEG) data. In this paper, a window selection method based on …

Emotion effects in event-related brain potentials (ERPs) have previously been reported for a
range of visual stimuli, including emotional words, pictures, and facial expressions. Still, little …

The conflicting findings from the few studies conducted with regard to gender differences in
the recognition of vocal expressions of emotion have left the exact nature of these …

Faces that move contain rich information about facial form, such as facial features and their
configuration, alongside the motion of those features. During social interactions, humans …

Deep learning has demonstrated remarkable performance in emotion recognition tasks
based on Electroencephalogram (EEG) signals. The utilization of the spatial and temporal …

We investigated the minimum expressive intensity that is required to recognize (above
chance) static and dynamic facial expressions of happiness, sadness, anger, disgust, fear …

In the current study, electroencephalography (EEG) was recorded simultaneously with facial
electromyography (fEMG) to determine whether emotional faces and emotional scenes are …