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 …

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 …

In recent years, graph convolutional neural networks have become research focus and
inspired new ideas for emotion recognition based on EEG. Deep learning has been widely …

Assessment of the cognitive functions and state of clinical subjects is an important aspect of
e-health care delivery, and in the development of novel human-machine interfaces. A …

In this paper, a multichannel EEG emotion recognition method based on a novel dynamical
graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed …

Emotions have an important role in daily life, not only in human interaction, but also in
decision-making processes, and in the perception of the world around us. Due to the recent …

Deep learning has recently achieved remarkable success in emotion recognition based on
Electroencephalogram (EEG), in which convolutional neural networks (CNNs) are the mostly …

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

Recently, deep learning methodologies have become popular to analyse physiological
signals in multiple modalities via hierarchical architectures for human emotion recognition …

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a
user, which is considered an important factor in human-machine-interaction. Many methods …