A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition

By Yang Li, Wenming Zheng, Yuan Zong, Zhen Cui, Tong Zhang, Xiaoyan Zhou. IEEE Transactions on Affective Computing, 2021

311 citations recorded by the source corpus.

In this paper, we propose a novel neural network model, called bi-hemisphere domain adversarial neural network (BiDANN) model, for electroencephalograph (EEG) emotion recognition. The BiDANN model is inspired by the neuroscience findings that the left and right hemispheres of human's brain are asymmetric to the emotional response. It contains a global and two local domain discriminators that work adversarially with a classifier to learn discriminative emotional features for each hemisphere. At the same time, it tries to reduce the possible domain differences in each hemisphere between the source and target domains so as to improve the generality of the recognition model. In addition, we also propose an improved version of BiDANN, denoted by BiDANN-S, for subject-independent EEG emotion recognition problem by lowering the influences of the personal information of subjects to the EEG emotion recognition. Extensive experiments on the SEED database are conducted to evaluate the performance of both BiDANN and BiDANN-S. The experimental results have shown that the proposed BiDANN and BiDANN models achieve state-of-the-art performance in the EEG emotion recognition.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3e28d4357af48c48b7338bffeace90412690606f

How researchers see this paper

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

In Cognitive Neuroscience

1. Chunyan Miao
2. Di Wang
3. Peixiang Zhong
4. Yi Ding
5. Qiuhao Zeng
6. Su Zhang
7. Neethu Robinson
8. Tengfei Song

In Experimental and Cognitive Psychology

1. Chunyan Miao
2. Di Wang
3. Peixiang Zhong
4. Tengfei Song
5. Zhen Cui
6. Yuan Zong
7. Jason Teo
8. Tong Zhang

In Signal Processing

1. Tengfei Song
2. Zhen Cui
3. Cuntai Guan
4. Jason Teo
5. Chunyan Miao
6. James Mountstephens
7. Wenming Zheng
8. Nazmi Sofian Suhaimi

Citations in the corpus

• EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, methodology): “For SEED, we follow the experimental settings in [7], [12], [16] to evaluate our RGNN model using subject-dependent classification.”
• TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition (2021, methodology): “…fusion to detect emotions from EEG. Recently, deep learning-based methods have shown promising results in the BCI domain, such as motor imagery clas-sification [15], [16], [17], [18], [19], emotion recognition [20], [21], [22], [23], [24], [25], and mental-task classification [26], [27], [28].”
• EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities (2020, background): “Researchers have highlighted the significant challenges posed by intersubject classification in affective computing [140, 142–147].”
• A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition (2019, background): “Li et al. [18] separately extracted two brain hemispheric features and achieved the state-of-the-art classification performance.”
• Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives (2022, methodology): “Zellinger et al. (2017) compare their CMD methods with other discrepancy-based methods, e.g., DDC (Tzeng et al., 2014), deep CROAL (Sun and Saenko, 2016), DLID (Chopra et al., 2013), AdaBN (Li et al., 2016)) and adversarial DANN (Ganin and Lempitsky, 2015).”
• Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, unknown): “Li et al. [100] further proposed an improved bi-hemisphere domain adversarial neural network (BiDANN-S) for subject-independent emotion classification.”