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Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity
IEEE Transactions on Cognitive and Developmental Systems ( IF 5 ) Pub Date : 2020-06-01 , DOI: 10.1109/tcds.2019.2949306
Jinpeng Li , Shuang Qiu , Changde Du , Yixin Wang , Huiguang He

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.

中文翻译:

基于潜在表征相似性的脑电情感识别领域自适应

情绪识别在现实世界中有许多潜在的应用。在众多的情绪识别方法中,脑电图(EEG)在可靠性和准确性方面表现出优势。然而,脑电图的个体差异限制了情绪分类器跨学科的泛化。此外,由于脑电图的非平稳特性,一个对象的信号会随着时间的推移而变化,这对于获得可以跨会话工作的模型是一个挑战。在本文中,我们提出了一种新的领域适应方法来概括跨主题和会话的情绪识别模型。我们使用神经网络来实现情感识别模型,该模型通过最小化源的分类错误同时使源和目标的潜在表示相似来优化。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适应联合分布。该方法与基准 SEED 和 DEAP 上的多个代表和最近的领域自适应算法进行了比较,分别用于识别三个和四个情感状态。实验结果表明,所提出的方法达到并优于现有技术。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适应联合分布。该方法与基准 SEED 和 DEAP 上的多个代表和最近的领域自适应算法进行了比较,分别用于识别三个和四个情感状态。实验结果表明,所提出的方法达到并优于现有技术。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适应联合分布。该方法与基准 SEED 和 DEAP 上的多个代表和最近的领域自适应算法进行了比较,分别用于识别三个和四个情感状态。实验结果表明,所提出的方法达到并优于现有技术。该方法与基准 SEED 和 DEAP 上的多个代表和最近的领域自适应算法进行了比较,分别用于识别三个和四个情感状态。实验结果表明,所提出的方法达到并优于现有技术。该方法与基准 SEED 和 DEAP 上的多个代表和最近的领域自适应算法进行了比较,分别用于识别三个和四个情感状态。实验结果表明,所提出的方法达到并优于现有技术。
更新日期:2020-06-01
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