Emotion recognition is the ability to precisely infer human emotions from numerous sources
and modalities using questionnaires, physical signals, and physiological signals. Recently …

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

Affective computing, a subcategory of artificial intelligence, detects, processes, interprets,
and mimics human emotions. Thanks to the continued advancement of portable non …

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 …

Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …

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 …

Smart healthcare is a framework that utilizes technologies such as wearable devices, the
Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless …

Emotion recognition based on electroencephalography (EEG) is a significant task in the
brain-computer interface field. Recently, many deep learning-based emotion recognition …

In recent years, the rapid advances in machine learning (ML) and information fusion has
made it possible to endow machines/computers with the ability of emotion understanding …