The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
EEG-based emotion classification using deep belief networks
Abstract
In recent years, there are many great successes in using deep architectures for unsupervised feature learning from data, especially for images and speech. In this paper, we introduce recent advanced deep learning models to classify two emotional categories (positive and negative) from EEG data. We train a deep belief network (DBN) with differential entropy features extracted from multichannel EEG as input. A hidden markov model (HMM) is integrated to accurately capture a more reliable emotional stage switching. We also compare the performance of the deep models to KNN, SVM and Graph regularized Extreme Learning Machine (GELM). The average accuracies of DBN-HMM, DBN, GELM, SVM, and KNN in our experiments are 87.62%, 86.91%, 85.67%, 84.08%, and 69.66%, respectively. Our experimental results show that the DBN and DBN-HMM models improve the accuracy of EEG-based emotion classification in comparison with the state-of-the-art methods.
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“Zheng et al. [167] found that DBNs can improve recognition accuracy of models based on differential entropy features.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [167]
Citations
Cites
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893 · cited 1×
“However, due to the fuzzy boundaries of emotion, the detection and modeling of emotion using artificial intelligence and machine learning techniques still remains a big challenge and many open research questions are still remained [2].”
BackgroundDifferential entropy feature for EEG-based emotion classification
Duan, R.-N., Zhu, J., & Lu, B. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6695876 · Cited by 1,225
Self-citationRay, W. J., & Cole, H. W. (1985) · Science · doi:10.1126/science.3992243 · Cited by 1,146
Emotional state classification from EEG data using machine learning approach
Wang, X.-W., Nie, D., & Lu, B.-L. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.06.046 · Cited by 755
Self-citationMultimodal Emotion Recognition in Response to Videos
Soleymani, M., Pantić, M., & Pun, T. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.37 · Cited by 740
Emotion classification based on gamma-band EEG
Li, M., & Lu, B. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5334139 · Cited by 413
Self-citationLearning deep physiological models of affect
Martinez, H. P., Bengio, Y., & Yannakakis, G. N. (2013) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2013.2247823 · Cited by 287
Sleep Stage Classification Using Unsupervised Feature Learning
Längkvist, M., Karlsson, L., & Loutfi, A. (2012) · Advances in Artificial Neural Systems · doi:10.1155/2012/107046 · Cited by 248
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Research works printed but not linked: 1
- peng, 0, Discriminative graph regularized extreme learning machine for face recognition, Neurocomputing
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Zheng, W.-L., & Lu, B.-L. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2431497 · Cited by 2,477
Self-citationEEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
Song, T., Zheng, W., Song, P., & Cui, Z. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2817622 · Cited by 1,646
Self-citationDeep learning-based electroencephalography analysis: a systematic review
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
Zheng, W.-L., Liu, W., Lu, Y., Lu, B.-L., & Cichocki, A. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2797176 · Cited by 1,276
Self-citationDeep learning for healthcare applications based on physiological signals: A review
Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.005 · Cited by 1,081
Metadata
- Authors
- Zheng, W.-L., Zhu, J.-Y., Peng, Y., & Lu, B.-L.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Venue
- 2014 IEEE International Conference on Multimedia and Expo (ICME)
- Publisher
- IEEE
- Published
- 2014-07
- Pages
- 1-6
- OpenAlex
- W2004104731
- Cited by
- 367
- References
- 8
- Citations by year
- 2026: 10 · 2025: 32 · 2024: 35 · 2023: 37 · 2022: 53 · 2021: 41 · 2020: 39 · 2019: 43 · 2018: 36 · 2017: 22 · 2016: 10 · 2015: 6 · 2014: 3
- FWCI
- 22.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Multimedia and Expo (ICME) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- EEG-based emotion recognition, deep belief network, hidden Markov models, differential entropy features, multichannel EEG, positive and negative emotions, support vector machine, k-nearest neighbors
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