Emotion recognition technology through analyzing the EEG signal is currently an essential
concept in Artificial Intelligence and holds great potential in emotional health care, human …

Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …

Due to the limited perceptual field, convolutional neural networks (CNN) only extract local
temporal features and may fail to capture long-term dependencies for EEG decoding. In this …

Multimodal signals are powerful for emotion recognition since they can represent emotions
comprehensively. In this article, we compare the recognition performance and robustness of …

Emotion recognition based on electroencephalography (EEG) is of great important in the
field of Human–Computer Interaction (HCI), which has received extensive attention in recent …

A brain–computer interface (BCI) enables a user to communicate with a computer directly
using brain signals. The most common noninvasive BCI modality, electroencephalogram …

Even with an unprecedented breakthrough of deep learning in electroencephalography
(EEG), collecting adequate labelled samples is a critical problem due to laborious and time …

The high temporal resolution and the asymmetric spatial activations are essential attributes
of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the …

Neuroscience research studies have shown that the left and right hemispheres of the human
brain response differently to the same or different emotions. Exploiting this difference in the …

Textual language is the most natural carrier of human emotion. In natural language
processing, textual emotion recognition (TER) has become an important topic due to its …