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Interpretable emotion recognition using EEG signals

Qing, Chunmei; Qiao, Rui; Xu, Xiangmin; Cheng, Yongqiang


Chunmei Qing

Rui Qiao

Xiangmin Xu


Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests in recent years and has been broadly adopted in medical, affective computing, and other relevant fields. However, the majority of the research reported in this field tends to focus on the accuracy of classification whilst neglecting the interpretability of emotion progression. In this paper, we propose a new interpretable emotion recognition approach with the activation mechanism by using machine learning and EEG signals. This paper innovatively proposes the emotional activation curve to demonstrate the activation process of emotions. The algorithm first extracts features from EEG signals and classifies emotions using machine learning techniques, in which different parts of a trial are used to train the proposed model and assess its impact on emotion recognition results. Second, novel activation curves of emotions are constructed based on the classification results, and two emotion coefficients, i.e., the correlation coefficients and entropy coefficients. The activation curve can not only classify emotions but also reveals to a certain extent the emotional activation mechanism. Finally, a weight coefficient is obtained from the two coefficients to improve the accuracy of emotion recognition. To validate the proposed method, experiments have been carried out on the DEAP and SEED dataset. The results support the point that emotions are progressively activated throughout the experiment, and the weighting coefficients based on the correlation coefficient and the entropy coefficient can effectively improve the EEG-based emotion recognition accuracy.


Qing, C., Qiao, R., Xu, X., & Cheng, Y. (2019). Interpretable emotion recognition using EEG signals. IEEE Access, 7, 94160-94170.

Journal Article Type Article
Acceptance Date Jun 27, 2019
Online Publication Date Jul 15, 2019
Publication Date Jul 15, 2019
Deposit Date Aug 9, 2019
Publicly Available Date Aug 9, 2019
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 7
Pages 94160-94170
Keywords EEG; Emotion activation; Emotion recognition; Machine learning; Electroencephalography; Feature extraction; Brain modeling; Physiology; Computational modeling; Human computer interaction
Public URL
Publisher URL
Additional Information This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see


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Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see

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