Emotional state classification from EEG data using machine learning approach
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The change of emotions is a temporal dependent process.
“The change of emotions is a temporal dependent process.”
From Multimodal Emotion Recognition Using Deep Neural Networks (2017) · cites this paper as [11]
Cites
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 · see citing works
Measuring emotion: The self-assessment manikin and the semantic differential
Bradley, M. M., & Lang, P. J. (1994) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/0005-7916(94)90063-9 · Cited by 9,538 · see citing works
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works
Emotion recognition system using short-term monitoring of physiological signals
Kim, K. H., Bang, S. W., & Kim, S. R. (2004) · Medical & Biological Engineering & Computing · doi:10.1007/bf02344719 · Cited by 944 · see citing works
Davidson, R. J. (1998) · Psychophysiology · doi:10.1017/s0048577298000134 · Cited by 646 · see citing works
Hewig, J., Hagemann, D., Seifert, J., Gollwitzer, M., Naumann, E., & Bartussek, D. (2005) · Cognition & Emotion · doi:10.1080/02699930541000084 · Cited by 246 · see citing works
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Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.011 · Cited by 806 · see citing works
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
EEG-based emotion classification using deep belief networks
Zheng, W.-L., Zhu, J.-Y., Peng, Y., & Lu, B.-L. (2014) · 2014 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2014.6890166 · Cited by 367 · see citing works
Self-citationEmotion Recognition Using Multimodal Deep Learning
Liu, W., Zheng, W.-L., & Lu, B.-L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46672-9_58 · Cited by 301 · 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-citationMultimodal Emotion Recognition Using Deep Neural Networks
Tang, H., Liu, W., Zheng, W.-L., & Lu, B.-L. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-70093-9_86 · Cited by 143 · see citing works
Self-citationAffective states classification using EEG and semi-supervised deep learning approaches
Xu, H., & Plataniotis, K. N. (2016) · 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2016.7813351 · Cited by 86 · see citing works
Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals
Lu, Y., Wang, M., Wu, W., Han, Y., Zhang, Q., & Chen, S. (2020) · Measurement · doi:10.1016/j.measurement.2019.107003 · Cited by 73 · see citing works
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 · see citing works
Self-citationCore-Brain-Network-Based Multilayer Convolutional Neural Network for Emotion Recognition
Gao, Z., Li, R., Ma, C., Rui, L., & Sun, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3090164 · Cited by 61 · see citing works
Multimodal mild depression recognition based on EEG-EM synchronization acquisition network
Zhu, J., Wang, Y., La, R., Zhan, J., Niu, J., Zeng, S., & Hu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2901950 · Cited by 56 · see citing works
Zhai, X., Fang, Q., Dong, Y., Wei, Z., Yuan, J., Cacciolatti, L., & Yang, Y. (2018) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12284 · Cited by 26 · see citing works
Pham, N., Jia, H., Tran, M., Dinh, T., Bui, N., Kwon, Y., Ma, D., Nguyen, P., Mascolo, C., & Vu, T. (2022) · Proceedings of the 28th Annual International Conference on Mobile Computing And Networking · doi:10.1145/3495243.3560533 · Cited by 12 · see citing works
Tang, C., Li, Y., & Chen, B. (2022) · Brain Science Advances · doi:10.26599/bsa.2022.9050013 · Cited by 10 · see citing works
- Authors
- Wang, X.-W., Nie, D., & Lu, B.-L.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Lu, Bao-Liang
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2014-04
- Volume
- 129
- Pages
- 94-106
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2139564752
- Cited by
- 755
- References
- 6
- Citations by year
- 2026: 35 · 2025: 59 · 2024: 78 · 2023: 87 · 2022: 82 · 2021: 89 · 2020: 75 · 2019: 71 · 2018: 61 · 2017: 48 · 2016: 35 · 2015: 25 · 2014: 8 · 2013: 1
- FWCI
- 28.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (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 › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- EEG-based emotion recognition, EEG features, power spectrum features, feature smoothing, manifold learning, brain-computer interface
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