EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

By Peixiang Zhong, Di Wang, Chunyan Miao. IEEE Transactions on Affective Computing, 2019

779 citations recorded by the source corpus.

Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this article, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED, and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/0c57e17102896e9bf356e89d5daca93f8ef7a2f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Xiaorong Gao
2. Qingqing Zheng
3. Bingchuan Liu
4. Yonghao Song

In Cognitive Neuroscience

1. Bin Hu
2. Xiaorong Gao
3. Prayag Tiwari
4. Yazhou Zhang
5. Meihong Yang
6. Qingqing Zheng
7. Xiang Li
8. Zhigang Zhao

In Experimental and Cognitive Psychology

1. Bin Hu
2. Xiaorong Gao
3. Prayag Tiwari
4. Yazhou Zhang
5. Meihong Yang
6. Qingqing Zheng
7. Xiang Li
8. Zhigang Zhao

Citations in the corpus

• EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization (2022, methodology): “Besides, graph-based neural networks learning the intrinsic relationship among different EEG channels such as dynamical graph convolutional neural network (DGCNN) [41] and regularized graph neural network (RGNN) [43] are also included for comparison.”
• EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “[199] SEED a regularized GNN model (RGNN) that adopts two kinds of regularizers: NodeDAT and EmotionDL 3 classes, subject-dependent modeling: 94.”
• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “[333] designed a regularized graph neural network (RGNN) for EEG-based emotion recognition.”