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 …

Emotion recognition based on physiological signals has been a hot topic and applied in
many areas such as safe driving, health care and social security. In this paper, we present 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 …

To investigate critical frequency bands and channels, this paper introduces deep belief
networks (DBNs) to constructing EEG-based emotion recognition models for three emotions …

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 …

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 …

Emotions are fundamental for human beings and play an important role in human cognition.
Emotion is commonly associated with logical decision making, perception, human …

Electroencephalography (EEG) measures the neuronal activities in different brain regions
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …

In this paper, we present a multimodal emotion recognition framework called EmotionMeter
that combines brain waves and eye movements. To increase the feasibility and wearability …

In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for
emotion recognition using a machine learning approach. Up to now, various findings of …