The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network
Abstract
Automatic emotion recognition based on multi-channel neurophysiological signals, as a challenging pattern recognition task, is becoming an important computer-aided method for emotional disorder diagnoses in neurology and psychiatry. Traditional approaches require designing and extracting a range of features from single or multiple channel signals based on extensive domain knowledge. This may be an obstacle for non-domain experts. Moreover, traditional feature fusion method can not fully utilize correlation information between different channels. In this paper, we propose a preprocessing method that encapsulates the multi-channel neurophysiological signals into grid-like frames through wavelet and scalogram transform. We further design a hybrid deep learning model that combines the `Convolutional Neural Network (CNN)' and `Recurrent Neural Network (RNN)', for extracting task-related features, mining inter-channel correlation and incorporating contextual information from those frames. Experiments are carried out, in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Our results demonstrate the effectiveness of the proposed methods, with respect to the emotional dimensions of Valence and Arousal.
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Cites
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“After each trial, the subjects subjectively evaluate their emotional experience on a two-dimensional emotional space which is proposed by Russell [19], where the two dimensions are Arousal (it ranges from relaxed to aroused) and Valence (it ranges from pleasant to unpleasant) respectively.”
BackgroundLearning long-term dependencies with gradient descent is difficult
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DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093
Supervised Sequence Labelling with Recurrent Neural Networks
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Cited by
Deep learning for electroencephalogram (EEG) classification tasks: a review
Craik, A., He, Y., & Contreras-Vidal, J. L. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0ab5 · Cited by 1,758
A review of emotion recognition using physiological signals
Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., & Yang, X. (2018) · Sensors · doi:10.3390/s18072074 · Cited by 923
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Zhong, P., Wang, D., & Miao, C. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2994159 · Cited by 910
EEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508
Self-citationEEG emotion recognition using fusion model of graph convolutional neural networks and LSTM
Yin, Y., Zheng, X., Hu, B., Zhang, Y., & Cui, X. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106954 · Cited by 466
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Metadata
- Authors
- Li, X., Song, D., Zhang, P., Yu, G., Hou, Y., & Hu, B.
- Venue
- 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 352-359
- OpenAlex
- W2572280252
- Cited by
- 283
- References
- 21
- FWCI
- 19.35
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Li, X., Song, D., Zhang, P., Yu, G., Hou, Y., & Hu, B. (2016). Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). https://doi.org/10.1109/bibm.2016.7822545
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