Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity
Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity
复制标题
基于潜在表示相似性的脑电情感识别领域适应
DOI：
10.1109/tcds.2019.2949306
复制
发表时间：
2020-06-01
影响因子：
5
通讯作者：
He, Huiguang
中科院分区：
文献类型：
--
作者：
Li, Jinpeng;Qiu, Shuang;He, Huiguang
情感识别在真实的世界中有许多潜在的应用。在众多的情绪识别方法中，脑电（EEG）在可靠性和准确性方面具有优势。然而，脑电信号的个体差异限制了情绪分类器的跨学科推广。此外，由于EEG的非平稳特性，一个对象的信号随时间变化，这是一个挑战，以获取模型，可以跨会话工作。在这篇文章中，我们提出了一种新的领域自适应方法来概括跨学科和会话的情感识别模型。我们使用神经网络来实现情感识别模型，通过最小化源的分类错误，同时使源和目标在潜在表示中相似来优化这些模型。考虑到网络层的功能差异，我们使用对抗训练来适应早期层的边缘分布，并执行关联强化来适应最后一层的条件分布。通过这种方式，我们通过同时适应边缘分布和条件分布来近似地适应联合分布。该方法与多个代表和最近的域自适应算法的基准种子和DEAP识别三个和四个情感状态，分别进行了比较。实验结果表明，该方法达到并优于现有技术水平。
Emotion recognition has many potential applications in the real world. Among the many emotion recognition methods, electroencephalogram (EEG) shows advantage in reliability and accuracy. However, the individual differences of EEG limit the generalization of emotion classifiers across subjects. Moreover, due to the nonstationary characteristic of EEG, the signals of one subject change over time, which is a challenge to acquire models that could work across sessions. In this article, we propose a novel domain adaptation method to generalize the emotion recognition models across subjects and sessions. We use neural networks to implement the emotion recognition models, which are optimized by minimizing the classification error on the source while making the source and the target similar in their latent representations. Considering the functional differences of the network layers, we use adversarial training to adapt the marginal distributions in the early layers and perform association reinforcement to adapt the conditional distributions in the last layers. In this way, we approximately adapt the joint distributions by simultaneously adapting marginal distributions and conditional distributions. The method is compared with multiple representatives and recent domain adaptation algorithms on benchmark SEED and DEAP for recognizing three and four affective states, respectively. The experimental results show that the proposed method reaches and outperforms the state of the arts.