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Multisource Wasserstein Adaptation Coding Network for EEG emotion recognition
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AI
DOI：10.1016/j.bspc.2022.103687.png)
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摘要
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Emotion recognition has an important application in human-computer interaction (HCI). Electroencephalogram (EEG) is a reliable method in emotion recognition and is widely studied. However, since the individual variability of EEG, it is difficult to build a generic model between different subjects. In addition, the EEG signals will change in different periods, which has a great impact on the model. Therefore, building an effective model for cross-sessions, cross-subjects, cross- subjects and sessions has become challenging. In order to solve this problem, we propose a new emotion recognition method called Multisource Wasserstein Adaptation Coding Network (MWACN). MWACN can simplify output data and retain important information of input data by Autoencoder. It also uses Wasserstein distance and Association Reinforcement to adapt marginal distribution and conditional distribution. We validated the effectiveness of the model on SEED dataset and SEED-IV dataset. In cross-sessions experiment, the accuracy of our model achieves 92.08% in SEED and 76.04% in SEED-IV. In cross-subjects experiment, the accuracy of our model achieves 87.59% in SEED and 74.38% in SEED-IV. In cross-subjects and sessions experiment, the accuracy of the MWACN is improved by 3.72% in SEED and 5.88% in SEED-IV. The results show that our proposed MWACN outperforms recent domain adaptation algorithms.
Keyword:
Emotion recognition
EEG
Transfer learning
Deep learning
Domain adaptation
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
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引用论文
Emotion Recognition from Single-Trial EEG Based on Kernel Fisher's Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine基于核Fisher情感模式和不平衡拟共形核支持向量机的单次脑电情感识别.png)
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OAAI
SENSORSIF3.5
OAAI分享收藏分享收藏Emotion recognition and its application to computer agents with spontaneous interactive capabilities
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