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EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks

The proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels via training a neural network so as to benefit for more discriminative EEG feature extraction.
IEEE (opens in a new tab)

A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition

The effectiveness and advantage of the proposed BiHDM model in solving the EEG emotion recognition problem are demonstrated and the important brain areas in emotion expression are investigated.
IEEE (opens in a new tab)

MPED: A Multi-Modal Physiological Emotion Database for Discrete Emotion Recognition

A multi-modal physiological emotion database is designed and built, which collects four modal physiological signals, i.e., electroencephalogram (EEG), galvanic skin response, respiration, and electrocardiogram (ECG), and a novel attention-long short-term memory (A-LSTM), which strengthens the effectiveness of useful sequences to extract more discriminative features.
IEEE (opens in a new tab)

Hybrid Message Passing with Performance-Driven Structures for Facial Action Unit Detection

A novel hybrid message passing neural network with performance-driven structures (HMP-PS), which combines complementary message passing methods and captures more possible structures in a Bayesian manner is proposed.
IEEE (opens in a new tab)

Uncertain Graph Neural Networks for Facial Action Unit Detection

This work proposes an uncertain graph neural network (UGN) to learn the probabilistic mask that simultaneously captures both the individual dependencies among AUs and the uncertainties and proposes an adaptive weighted loss function based on the epistemic uncertainties to adaptively vary the weights of the training samples during the training process to account for unbalanced data distributions among AU.
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Variational Instance-Adaptive Graph for EEG Emotion Recognition

A variational instance-adaptive graph method (V-IAG) that simultaneously captures the individual dependencies among different EEG electrodes and estimates the underlying uncertain information and combines these two types of graphs to extract more discriminative features.
IEEE (opens in a new tab)

Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition

A constraint optimization method is proposed to encode the generic knowledge on expression-AUs probabilistic dependencies into a Bayesian Network (BN), then integrated into a deep learning framework as a weak supervision for an AU detection model.
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Instance-Adaptive Graph for EEG Emotion Recognition

A novel instance-adaptive graph method (IAG), which employs a more flexible way to construct graphic connections so as to present different graphic representations determined by different input instances, which achieves the state-of-the-art performance.
Publisher (opens in a new tab)

Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation

A novel dynamic probabilistic graph convolution (DPG) model is proposed to simultaneously exploit AU appearances, AU dynamics, and their semantic structural dependencies for AU intensity estimation to capture the inherent dependencies among AUs.
IEEE (opens in a new tab)

Graph-Embedded Convolutional Neural Network for Image-Based EEG Emotion Recognition

This article proposes a novel method to generate continuous images from discrete EEG signals by introducing offset variables following a Gaussian distribution for each EEG channel to alleviate the biased electrode coordinates during image generation.
IEEE (opens in a new tab)
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