A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition
A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition
复制标题
用于脑电情绪识别的双半球域对抗神经网络模型
DOI：
10.1109/taffc.2018.2885474
复制
发表时间：
2021-04-01
影响因子：
11.2
通讯作者：
Zhou, Xiaoyan
中科院分区：
文献类型：
--
作者：
Li, Yang;Zheng, Wenming;Zhou, Xiaoyan
本文提出了一种新的神经网络模型，称为双半球域对抗神经网络（BiDANN）模型，用于脑电（EEG）情感识别。BiDANN模型的灵感来自于神经科学的发现，即人类大脑的左半球和右半球对情绪反应是不对称的。它包含一个全局和两个局部域鉴别器，它们与分类器反向工作，以学习每个半球的鉴别性情感特征。同时，它试图减少源域和目标域之间在每个半球上可能存在的域差异，从而提高识别模型的通用性。此外，我们还提出了一个改进的版本的BiDANN，表示为BiDANN-S，主体无关的EEG情感识别问题，通过降低的影响，个人信息的主体的EEG情感识别。在SEED数据库上进行了大量的实验，以评估BiDANN和BiDANN-S的性能。实验结果表明，所提出的BiDANN和BiDANN模型在脑电情感识别中具有较好的性能。
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.