Abstract
Affective models based on EEG signals have been proposed in recent years. However, most of these models require subject-specific training and generalize worse when they are applied to new subjects. This is mainly caused by the individual differences across subjects. While, on the other hand, it is time-consuming and high cost to collect subject-specific training data for every new user. How to eliminate the individual differences in EEG signals for implementation of affective models is one of the challenges. In this paper, we apply Deep adaptation network (DAN) to solve this problem. The performance is evaluated on two publicly available EEG emotion recognition datasets, SEED and SEED-IV, in comparison with two baseline methods without domain adaptation and several other domain adaptation methods. The experimental results indicate that the performance of DAN is significantly superior to the existing methods.
H. Li and Y.-M. Jin—The first two authors contributed equally to this work.
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Notes
- 1.
The SEED and SEED-IV datasets are available at http://bcmi.sjtu.edu.cn/~seed/index.html.
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Acknowledgement
This work was supported in part by the grants from the National Key Research and Development Program of China (Grant No. 2017YF-B1002501), the National Natural Science Foundation of China (Grant No. 6167-3266), and the Fundamental Research Funds for the Central Universities.
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Li, H., Jin, YM., Zheng, WL., Lu, BL. (2018). Cross-Subject Emotion Recognition Using Deep Adaptation Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_36
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