Differential entropy feature for EEG-based vigilance estimation
Abstract
This paper proposes a novel feature called differential entropy for EEG-based vigilance estimation. By mathematical derivation, we find an interesting relationship between the proposed differential entropy and the existing logarithm energy spectrum. We present a physical interpretation of the logarithm energy spectrum which is widely used in EEG signal analysis. To evaluate the performance of the proposed differential entropy feature for vigilance estimation, we compare it with four existing features on an EEG data set of twenty-three subjects. All of the features are projected to the same dimension by principal component analysis algorithm. Experiment results show that differential entropy is the most accurate and stable EEG feature to reflect the vigilance changes.
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DE features are a commonly used and more effective type of feature extraction for EEG emotion recognition.
“DE features are currently a commonly used and more effective type of feature extraction for EEG emotion recognition (Shi et al. 2013).”
From EEG-based emotion recognition with autoencoder feature fusion and MSC-TimesNet model (2026) · cites this paper as Shi et al. 2013
Cites
Destexhe, A., Contreras, D., & Steriade, M. (1999) · The Journal of Neuroscience · doi:10.1523/jneurosci.19-11-04595.1999 · Cited by 573 · see citing works
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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 · see citing works
A multimodal approach to estimating vigilance using EEG and forehead EOG
Zheng, W.-L., & Lu, B.-L. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa5a98 · Cited by 424 · see citing works
Self-citationData augmentation for EEG-based emotion recognition with deep convolutional neural networks
Wang, F., Zhong, S.-H., Peng, J., Jiang, J., & Liu, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-73600-6_8 · Cited by 256 · see citing works
Continuous Convolutional Neural Network with 3D Input for EEG-Based Emotion Recognition
Yang, Y., Wu, Q., Fu, Y., & Chen, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-04239-4_39 · Cited by 197 · see citing works
Data augmentation for enhancing EEG-based emotion recognition with deep generative models.
Luo, Y., Zhu, L.-Z., Wan, Z.-Y., & Lu, B.-L. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abb580 · Cited by 162 · see citing works
Self-citationA 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 · see citing works
Recognition of persisting emotional valence from EEG using convolutional neural networks
Yanagimoto, M., & Sugimoto, C. (2016) · 2016 IEEE 9th International Workshop on Computational Intelligence and Applications (IWCIA) · doi:10.1109/iwcia.2016.7805744 · Cited by 43 · see citing works
EEG-based emotion recognition with autoencoder feature fusion and MSC-TimesNet model
Yin, J., Qiao, Z., Han, L., & Zhang, X. (2026) · Computer Methods in Biomechanics and Biomedical Engineering · doi:10.1080/10255842.2025.2477801 · Cited by 41 · see citing works
- Authors
- Shi, L.-C., Jiao, Y.-Y., & Lu, B.-L.
- Affiliations
- Shanghai Jiao Tong University (CN)
- ORCID
- Bao-Liang Lu
- Venue
- 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2013-07
- Volume
- 2013
- Pages
- 6627-6630
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2038610431
- Cited by
- 396
- References
- 1
- Citations by year
- 2026: 15 · 2025: 51 · 2024: 76 · 2023: 68 · 2022: 57 · 2021: 46 · 2020: 31 · 2019: 16 · 2018: 13 · 2017: 11 · 2016: 4 · 2015: 3 · 2014: 3 · 2013: 2
- FWCI
- 6.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- differential entropy, EEG, EEG feature extraction, principal component analysis
- MeSH terms
- Adult, Algorithms, Arousal, Electroencephalography, Female, Humans, Male, Problem Solving, Signal Processing, Computer-Assisted
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