Abstract
Emotion is a subjective, conscious experience when people face different kinds of stimuli. In this paper, we adopt Deep Canonical Correlation Analysis (DCCA) for high-level coordinated representation to make feature extraction from EEG and eye movement data. Parameters of the two views’ nonlinear transformations are learned jointly to maximize the correlation. We propose a multi-view emotion recognition framework and evaluate its effectiveness on three real world datasets. We found that DCCA efficiently learned representations with high correlation, which contributed to higher emotion classification accuracy. Our experiment results indicate that DCCA model is superior to the state-of-the-art methods with mean accuracies of 94.58% on SEED dataset, 87.45% on SEED IV dataset, and 88.51% and 84.98% for four classification and two dichotomies on DEAP dataset, respectively.
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Acknowledgments
This work was supported in part by the grants from the National Key Research and Development Program of China (Grant No. 2017YFB1002501), the National Natural Science Foundation of China (Grant No. 61673266), and the Fundamental Research Funds for the Central Universities.
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Qiu, JL., Liu, W., Lu, BL. (2018). Multi-view Emotion Recognition Using Deep Canonical Correlation Analysis. 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_20
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