Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

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 …

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 …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

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 …

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …

Given the rapid development of brain–computer interfaces, emotion identification based on
EEG signals has emerged as a new study area with tremendous importance in recent years …

Construction workers' cognitive statuses affecting their safety and productivity are essential
for successful construction management. Electroencephalogram (EEG) provides a potential …

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 …

Interpreting and labeling human electroencephalogram (EEG) is a challenging task
requiring years of medical training. We present a framework for learning representations …