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.
“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]
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STILN: A novel spatial-temporal information learning network for EEG-based emotion recognition
Tang, Y., Wang, Y., Zhang, X., & Wang, Z. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104999 · Cited by 49 · see citing works
Emotion classification using single-channel scalp-EEG recording
Jalilifard, A., Pizzolato, E. B., & Islam, M. K. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590833 · Cited by 47 · see citing works
Deep Learning Algorithms in EEG Signal Decoding Application: A Review
Vallabhaneni, R. B., Sharma, P., Kumar, V., Kulshreshtha, V., Reddy, K. J., Kumar, S. S., Kumar, V. S., & Bitra, S. K. (2021) · IEEE Access · doi:10.1109/access.2021.3105917 · Cited by 46 · see citing works
- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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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