Open Access
Issue
ITM Web Conf.
Volume 53, 2023
2nd International Conference on Data Science and Intelligent Applications (ICDSIA-2023)
Article Number 01001
Number of page(s) 11
Section Artificial Intelligence
DOI https://doi.org/10.1051/itmconf/20235301001
Published online 01 June 2023
  1. American Psychological Association. (2021, October 26). Stress in America™ 2021: Pandemic Impedes Basic Decision-Making Ability. https://www.apa.org/news/press/releases/2021/10/stress-pandemic-decision-making. [Google Scholar]
  2. V. L. Kaundanya, A. Patil, and A. Panat. “Performance of k-NN classifier for emotion detection using EEG signals.” In 2015 International Conference on Communications and Signal Processing (ICCSP), pp. 1160-1164. IEEE, (2015). [Google Scholar]
  3. L. Jingxin, H. Meng, A. Nandi, and M. Li. “Emotion detection from EEG recordings.” In 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 1722-1727. IEEE, (2016). [Google Scholar]
  4. S. Katsigiannis, and N. Ramzan. “DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices.” IEEE journal of biomedical and health informatics 22, no. 1 (2017). [Google Scholar]
  5. V. Gupta, M. D. Chopda, and R. B. Pachori. “Cross-subject emotion recognition using flexible analytic wavelet transform from EEG signals.” IEEE Sensors Journal 19, no. 6 (2018). [Google Scholar]
  6. P. S. Ghare, and A. N. Paithane. “Human emotion recognition using non linear and non stationary EEG signal.” In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), pp. 1013-1016. IEEE, (2016). [Google Scholar]
  7. P. C. Petrantonakis, and L. J. Hadjileontiadis. “Emotion recognition from EEG using higher order crossings.” IEEE Transactions on Information Technology in Biomedicine 14, no. 2 (2009). [Google Scholar]
  8. A. Bhardwaj, A. Gupta, P. Jain, A. Rani, and J. Yadav. “Classification of human emotions from EEG signals using SVM and LDA Classifiers.” In 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 180-185. IEEE, (2015). [Google Scholar]
  9. C. Tommaso, M. D. Silvestri, M. Finedore, I. Haniff, and H. Esmailbeigi. “Emotion recognition for brain machine interface: non-linear spectral analysis of EEG signals using empirical mode decomposition.” In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 223-226. IEEE, (2018). [Google Scholar]
  10. M. A. Abdullah, and L. R. Christensen. “EEG Emotion Detection Using Multi-Model Classification.” In 2018 International Conference on Bioinformatics and Systems Biology (BSB), pp. 178-182. IEEE, (2018). [Google Scholar]
  11. Zhang, Hongli. “Expression-eeg based collaborative multimodal emotion recognition using deep autoencoder.” IEEE Access 8 (2020): 164130-164143. [CrossRef] [Google Scholar]
  12. H. Chao, and Y. Liu. “Emotion recognition from multi-channel EEG signals by exploiting the deep belief-conditional random field framework.” IEEE Access 8 (2020). [Google Scholar]
  13. T. Song, W. Zheng, P. Song, and Z. Cui. “EEG emotion recognition using dynamical graph convolutional neural networks.” IEEE Transactions on Affective Computing 11, no. 3 (2018). [Google Scholar]
  14. H. A. Gonzalez, J. Yoo, and I. M. Elfadel. “EEG-based emotion detection using unsupervised transfer learning.” In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 694-697. IEEE, 2019. [CrossRef] [Google Scholar]
  15. J. Li, S. Qiu, C. Du, Y. Wang, and H. He. “Domain adaptation for EEG emotion recognition based on latent representation similarity.” IEEE Transactions on Cognitive and Developmental Systems 12, no. 2 (2019). [Google Scholar]
  16. H. A. Gonzalez, S. Muzaffar, J. Yoo, and I. Abe M. Elfadel. “An inference hardware accelerator for EEG-based emotion detection.” In 2020 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-5. IEEE, (2020). [Google Scholar]
  17. S. Liu, X. Wang, L. Zhao, J. Zhao, Q. Xin, and S. Wang. “Subject-independent emotion recognition of EEG signals based on dynamic empirical convolutional neural network.” IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, no. 5 (2020). [Google Scholar]
  18. B. Wei, K. Hao, L. Gao, and X. Tang. “Bioinspired Visual-Integrated Model for Multilabel Classification of Textile Defect Images.” IEEE Transactions on Cognitive and Developmental Systems 13, no. 3 (2020). [Google Scholar]
  19. J. Cheng, M. Chen, C. Li, Y. Liu, R. Song, A. Liu, and X. Chen. “Emotion recognition from multi-channel eeg via deep forest.” IEEE Journal of Biomedical and Health Informatics 25, no. 2 (2020). [Google Scholar]
  20. H. Chao, and L. Dong. “Emotion recognition using three-dimensional feature and convolutional neural network from multichannel EEG signals.” IEEE sensors journal 21, no. 2 (2020). [Google Scholar]
  21. Y. Li, L. Wang, W. Zheng, Y. Zong, L. Qi, Z. Cui, T. Zhang, and T. Song. “A novel bihemispheric discrepancy model for eeg emotion recognition.” IEEE Transactions on Cognitive and Developmental Systems 13, no. 2 (2020). [Google Scholar]

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