Abstract
In the field of human-computer interaction, automatic emotion recognition is an important and challenging task. As a physiological signal that directly reflects the brain activity, EEG has advantages in emotion recognition. However, previous studies seldom consider together the temporal, spatial, and frequency characteristics of EEG signals, and the reported emotion recognition accuracy is not adequate for applications. To address this issue, this study proposes a new approach which extracts RASM as the feature to describe the frequency-space domain characteristics of EEG signals and constructs a LSTM network as the classifier to explore the temporal correlations of EEG signals. It is implemented on the DEAP dataset for a trial-level emotion recognition task. In a comparison with a number of relevant studies on DEAP, its mean accuracy of 76.67% ranks the first, which approves the effectiveness of this new approach.
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References
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (2002)
Chen, J., Hu, B., Xu, L., Moore, P., Su, Y.: Feature-level fusion of multimodal physiological signals for emotion recognition. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 395–399 (2015)
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: Deap: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)
Kollia, V.: Personalization effect on emotion recognition from physiological data: an investigation of performance on different setups and classifiers (2016)
Lang, P.J.: The emotion probe. Studies of motivation and attention. Am. Psychol. 50(5), 372 (1995)
Li, X., Song, D., Zhang, P., Yu, G., Hou, Y., Hu, B.: Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 352–359 (2017)
Lin, Y.P., Wang, C.H., Jung, T.P., Wu, T.L., Jeng, S.K., Duann, J.R., Chen, J.H.: EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57(7), 1798–1806 (2010)
Soleymani, M., Asghari-Esfeden, S., Fu, Y., Pantic, M.: Analysis of EEG signals and facial expressions for continuous emotion detection. IEEE Trans. Affect. Comput. 7(1), 17–28 (2016)
Verma, G.K., Tiwary, U.S.: Affect representation and recognition in 3D continuous valence-arousal-dominance space. Multimedia Tools Appl. 1–25 (2016)
Zheng, W.L., Zhu, J.Y., Lu, B.L.: Identifying stable patterns over time for emotion recognition from EEG (2016)
Acknowledgment
This work was supported by National Natural Science Founding of China (U1636218), the Guangzhou Key Lab of Body Data Science (201605030011) and the Science and Technology Program of Guangzhou of China (201704020043) and the Fundamental Research Funds for the Central Universities (2017MS041).
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Li, Z., Tian, X., Shu, L., Xu, X., Hu, B. (2018). Emotion Recognition from EEG Using RASM and LSTM. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_30
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DOI: https://doi.org/10.1007/978-981-10-8530-7_30
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