The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM
Abstract
Abstract In recent years, graph convolutional neural networks have become research focus and inspired new ideas for emotion recognition based on EEG. Deep learning has been widely used in emotion recognition, but it is still challenging to construct models and algorithms in practical applications. In this paper, we propose a novel emotion recognition method based on a novel deep learning model (ERDL). Firstly, EEG data is calibrated by 3s baseline data and divided into segments with 6s time window, and then differential entropy is extracted from each segment to construct feature cube. Secondly, the feature cube of each segment serves as input of the novel deep learning model which fuses graph convolutional neural network (GCNN) and long-short term memories neural networks (LSTM). In the fusion model, multiple GCNNs are applied to extract graph domain features while LSTM cells are used to memorize the change of the relationship between two channels within a specific time and extract temporal features, and Dense layer is used to attain the emotion classification results. At last, we conducted extensive experiments on DEAP dataset and experimental results demonstrate that the proposed method has better classification results than the state-of-the-art methods. We attained the average classification accuracy of 90.45% and 90.60% for valence and arousal in subject-dependent experiments while 84.81% and 85.27% in subject-independent experiments.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019) · Neural Computation · doi:10.1162/neco_a_01199 · Cited by 5,591
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
Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993) · Reviews of Modern Physics · doi:10.1103/revmodphys.65.413 · Cited by 4,684
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,613
A survey of decision tree classifier methodology
Safavian, S. R., & Landgrebe, D. (1991) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/21.97458 · Cited by 3,808
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
Song, T., Zheng, W., Song, P., & Cui, Z. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2817622 · Cited by 1,597
Cited by
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-citationEmotion recognition in EEG signals using deep learning methods: A review
Jafari, M., Shoeibi, A., Khodatars, M., Bagherzadeh, S., Shalbaf, A., García, D. L., Gorriz, J. M., & Acharya, U. R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107450 · Cited by 240
Automated emotion recognition: Current trends and future perspectives
Maithri, M., Raghavendra, U., Gudigar, A., Samanth, J., Barua, P. D., Murugappan, M., Chakole, Y., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106646 · Cited by 172
A multimodal emotion recognition method based on facial expressions and electroencephalography
Tan, Y., Sun, Z., Duan, F., Solé-Casals, J., & Caiafa, C. F. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103029 · Cited by 88
A Multi-Domain Adaptive Graph Convolutional Network for EEG-based Emotion Recognition
Li, R., Wang, Y., & Lu, B.-L. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475697 · Cited by 73
Metadata
- Authors
- Yin, Y., Zheng, X., Hu, B., Zhang, Y., & Cui, X.
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 100
- Pages
- 106954
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W3108087271
- Cited by
- 466
- References
- 63
- FWCI
- 51.59
- Type
- journal article
- Language
- en
- Open access
- closed
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