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 …

Emotion recognition based on electroencephalography (EEG) signal features is now one of
the booming big data research areas. As the number of commercial EEG devices in the …

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

The seminal work on Affective Computing in 1995 by Picard set the base for computing that
relates to, arises from, or influences emotions. Affective computing is a multidisciplinary field …

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

Emotion recognition based on multi-channel electroencephalograph (EEG) signals is
becoming increasingly attractive. However, the conventional methods ignore the spatial …

Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …

Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks
based on electroencephalography (EEG), and have achieved better performance than …

The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care
problems has received unprecedented attention in the last decade. The technique has …

Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …