As one of the most comprehensive and objective ways to describe facial expressions, the
Facial Action Coding System (FACS) has recently received significant attention. Over the …

With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

EEG signals have been reported to be informative and reliable for emotion recognition in
recent years. However, the inter-subject variability of emotion-related EEG signals still poses …

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 …

Electroencephalogram (EEG) has been widely used in emotion recognition due to its high
temporal resolution and reliability. Since the individual differences of EEG are large, the …

In this paper, we propose a novel Electroencephalograph (EEG) emotion recognition
method inspired by neuroscience with respect to the brain response to different emotions …

It is vital to develop general models that can be shared across subjects and sessions in the
real-world deployment of electroencephalogram (EEG) emotion recognition systems. Many …

Emotion recognition has many potential applications in the real world. Among the many
emotion recognition methods, electroencephalogram (EEG) shows advantage in reliability …

In this paper, we propose a novel neural network model, called bi-hemisphere domain
adversarial neural network (BiDANN) model, for electroencephalograph (EEG) emotion …

Facial expression recognition (FER) is a challenging task due to different expressions under
arbitrary poses. Most conventional approaches either perform face frontalization on a non …