Abstract
The change of emotions is a temporal dependent process. In this paper, a Bimodal-LSTM model is introduced to take temporal information into account for emotion recognition with multimodal signals. We extend the implementation of denoising autoencoders and adopt the Bimodal Deep Denoising AutoEncoder modal. Both models are evaluated on a public dataset, SEED, using EEG features and eye movement features as inputs. Our experimental results indicate that the Bimodal-LSTM model outperforms other state-of-the-art methods with a mean accuracy of 93.97%. The Bimodal-LSTM model is also examined on DEAP dataset with EEG and peripheral physiological signals, and it achieves the state-of-the-art results with a mean accuracy of 83.53%.
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Acknowledgments
This work was supported in part by grants from the National Key Research and Development Program of China (Grant No. 2017YFB1002501), the National Natural Science Foundation of China (Grant No. 61673266), the Major Basic Research Program of Shanghai Science and Technology Committee (Grant No. 15JC1400103), ZBYY-MOE Joint Funding (Grant No. 6141A02022604), and the Technology Research and Development Program of China Railway Corporation (Grant No. 2016Z003-B).
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Tang, H., Liu, W., Zheng, WL., Lu, BL. (2017). Multimodal Emotion Recognition Using Deep Neural Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_86
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DOI: https://doi.org/10.1007/978-3-319-70093-9_86
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