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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]
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BackgroundDifferential entropy feature for EEG-based emotion classification
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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