Emotion Recognition Using Multimodal Deep Learning
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Liu et al. and Zheng et al. incorporated eye movement data, obtaining accuracies of 91.01% and 73.59%, respectively.
“Additionally, Liu et al. [13] and Zheng et al. [12] incorporated eye movement data, obtaining accuracies of 91.01% and 73.59%, respectively.”
From Multimodal Emotion Recognition: Emotion Classification Through the Integration of EEG and Facial Expressions (2025) · cites this paper as [13]
On the SEED dataset, the Bimodal-LSTM model achieved the best accuracy of 93.97% among the compared models.
“Bimodal-LSTM achieves the best accuracy (93.97%)”
From Multimodal Emotion Recognition Using Deep Neural Networks (2017) · cites this paper as [6]
On the SEED dataset, the Bimodal-LSTM model's accuracy was about 2 percentage points higher than the state-of-the-art approaches.
“which is about 2% points higher than the state-of-the-art approaches”
From Multimodal Emotion Recognition Using Deep Neural Networks (2017) · cites this paper as [6]
Both Yin et al. and Liu et al. found that the representation learned through feature-level fusion leads to improved accuracy over individual modalities.
“Both studies found that the representation learned through feature-level fusion leads to improved accuracy over individual modalities.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [175]
Liu et al. used handcrafted features derived from EEG and eye tracking as input into a SAE.
“Similarly, Liu et al. [175] used handcrafted features derived from EEG and eye tracking as input into a SAE.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [175]
Cites
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093 · see citing works
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1× · see citing works
“Various algorithms can be used to train a RBM, such as Contrastive Divergence (CD) algorithm [12].”
Method(2011) · Cited by 2,752 · see citing works
Zheng, W.-L., & Lu, B.-L. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2431497 · Cited by 2,477 · see citing works
Self-citationDifferential 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 · see citing works
Self-citationMultimodal Learning with Deep Boltzmann Machines
Srivastava, N., & Salakhutdinov, R. (2012) · doi:10.5555/2627435.2697059 · Cited by 1,070 · see citing works
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 · see citing works
Self-citationEmotion 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 · see citing works
Self-citationVerma, G. K., & Tiwary, U. S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.007 · Cited by 354 · see citing works
Real-Time EEG-Based Human Emotion Recognition and Visualization
Liu, Y., Sourina, O., & Nguyen, M. K. (2010) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2010.37 · Cited by 313 · see citing works
Combining eye movements and EEG to enhance emotion recognition
Lu, Y., Zheng, W., Li, B., & Lu, B. (2015) · Cited by 185 · see citing works
Self-citationRobust EEG emotion classification using segment level decision fusion
Rozgić, V., Vitaladevuni, S. N. P., & Prasad, R. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6637858 · Cited by 116 · see citing works
EEG Based Emotion Identification Using Unsupervised Deep Feature Learning
Li, X., Zhang, P., Song, D., Yu, G., Hou, Y., & Hu, B. X. (2015) · Open Research Online - ORO (The Open University) · Cited by 67 · see citing works
Auxiliary information regularized machine for multiple modality feature learning
Yang, Y., Ye, H.-J., Zhan, D., et al. (2015) · International Conference on Artificial Intelligence · Cited by 16 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
Tzirakis, P., Trigeorgis, G., Nicolaou, M. A., Schuller, B., & Zafeiriou, S. (2017) · arXiv · doi:10.48550/arxiv.1704.08619 · Cited by 752 · see citing works
Emotion Recognition from Multiband EEG Signals Using CapsNet
Chao, H., Dong, L., Liu, Y., & Lu, B. (2019) · Sensors · doi:10.3390/s19092212 · Cited by 287 · see citing works
Self-citationSantamaria-Granados, L., Munoz-Organero, M., Ramirez-Gonzalez, G., Abdulhay, E., & Arunkumar, N. (2019) · IEEE Access · doi:10.1109/access.2018.2883213 · Cited by 289 · see citing works
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., & Zhang, Y. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc902 · Cited by 296 · see citing works
SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG
Xing, X., Li, Z., Xu, T., Shu, L., Hu, B., & Xu, X. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00037 · Cited by 269 · see citing works
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243 · see citing works
Multimodal 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-citationMultimodal Emotion Recognition from Eye Image, Eye Movement and EEG Using Deep Neural Networks
Guo, J.-J., Zhou, R., Zhao, L.-M., & Lu, B.-L. (2019) · 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856563 · 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
DeepVANet: A Deep End-to-End Network for Multi-modal Emotion Recognition
Zhang, Y., Hossain, M. Z., & Rahman, S. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-85613-7_16 · Cited by 29 · see citing works
Erdem Güler, S., & Patlar Akbulut, F. (2025) · IEEE Access · doi:10.1109/access.2025.3538642 · Cited by 21 · see citing works
Multimodal Emotion Recognition Using a Modified Dense Co-Attention Symmetric Network
Zhao, Z.-W., Liu, W., & Lu, B.-L. (2021) · 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) · doi:10.1109/ner49283.2021.9441352 · Cited by 19 · see citing works
Self-citation
- Authors
- Liu, W., Zheng, W.-L., & Lu, B.-L.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Lu, Bao-Liang
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 521-529
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2525648609
- Cited by
- 301
- References
- 14
- Citations by year
- 2026: 10 · 2025: 30 · 2024: 46 · 2023: 37 · 2022: 45 · 2021: 44 · 2020: 28 · 2019: 25 · 2018: 25 · 2017: 10 · 2016: 1
- FWCI
- 23.52
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (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 › Sentiment Analysis and Opinion Mining
- Keywords
- emotion recognition, multimodal deep learning, EEG, eye tracking, SEED dataset, DEAP dataset, physiological signals, complementary information
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 ›
Are you an author, or do you see a problem with this page? Request a review
