Recognition of emotions using multimodal physiological signals and an ensemble deep learning model

By Z. Yin, Mengyuan Zhao, Yongxiong Wang, Jingdong Yang, Jianhua Zhang. Comput. Methods Programs Biomed., 2017

381 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/143625e50b35bdda105fdaa3df0a2a04b34f6c28

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Wei-Long Zheng
2. Wei Liu
3. Bao-Liang Lu
4. Jielin Qiu
5. Thomas Martin Deserno
6. R. Swaminathan
7. A. Hammad
8. Essam H. Houssein

In Cognitive Neuroscience

1. Wei-Long Zheng
2. Wei Liu
3. Jielin Qiu
4. Bao-Liang Lu
5. Bin Hu
6. Xiang Li
7. Dawei Song
8. Prayag Tiwari

In Experimental and Cognitive Psychology

1. Wei-Long Zheng
2. Wei Liu
3. Jielin Qiu
4. Bao-Liang Lu
5. Bin Hu
6. Xiang Li
7. Dawei Song
8. Prayag Tiwari

Citations in the corpus

• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “Unlike the above three fusion strategies, model-level fusion uses HMM [33] or Bayesian networks [392] to establish the correlation between features of different modalities and one relaxed fusion mode.”
• Deep Learning on 1-D Biosignals: a Taxonomy-based Survey (2018, methodology): “Three papers [10, 104, 105] have used the publicly available DEAP database for the validation (see Appendix 2).”
• Ensemble deep learning: A review (2021, background): “2015 Convolutional channel features [66] Boosting Pedestrian detection, face detection, edge detection and object proposal generation 2015 Predicting drug side effects by multi-label learning and ensemble learning [198] Multi-label Predict the drug side effects 2016 Recognition of emotions using mul”
• Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition (2021, background): “Many deep learning models have been applied to emotion recognition in very recent years [28], [29], [30], [31], [32], [33], however, the characteristics these two kinds of models have not yet been fully studied.”
• EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “[207] proposed one Deep Learning-based stacking algorithm.”
• Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, background): “Behavior, speech, facial expressions, and physiological signals can all be used to identify human emotions [10–12].”