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All issues Volume 47 (2022) ITM Web Conf., 47 (2022) 02044 References
Open Access
Issue
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
Article Number 02044
Number of page(s) 10
Section Algorithm Optimization and Application
DOI https://doi.org/10.1051/itmconf/20224702044
Published online 23 June 2022
  1. H. S. Becker, “Notes on the concept of commitment,” American journal of Sociology, vol. 66, no. 1, pp. 32-40, 1960. [CrossRef] [Google Scholar]
  2. H. K. Jach, D. Feuerriegel, and L. D. Smillie, “Decoding Personality Trait Measures from Resting EEG: An Exploratory Report,” Cortex, vol. 130, 2020. [Google Scholar]
  3. A. R. Butt, A. Arsalan, and M. Majid, “Multimodal Personality Trait Recognition using Wearable Sensors in Response to Public Speaking,” IEEE Sensors Journal, vol. PP, no. 99, pp. 1-1, 2020. [Google Scholar]
  4. W. Li et al., “EEG responses to emotional videos can quantitatively predict big-five personality traits,” Neurocomputing, vol. 415, pp. 368-381, 2020. [CrossRef] [Google Scholar]
  5. F. Q. Annisa, E. Supriyanto, and S. Taheri, “Personality Dimensions Classification with EEG Analysis using Support Vector Machine,” in 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2020, pp. 79-82: IEEE. [Google Scholar]
  6. I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016. [Google Scholar]
  7. L. Ma, J. W. Minett, T. Blu, and W. S. Wang, “Resting state EEG-based biometrics for individual identification using convolutional neural networks,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 2848-2851: IEEE. [Google Scholar]
  8. D. Wen et al., “Feature classification method of resting-state EEG signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on multi-view convolutional neural network,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 8, pp. 1702-1709, 2020. [CrossRef] [Google Scholar]
  9. C. Szegedy et al., “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9. [Google Scholar]
  10. S. Zhao, “Research on Depression’s Real-time Monitoring Based on Electroencephalograph and Convolutional Neural Network,” Lanzhou University, 2018. [Google Scholar]
  11. Q. Luo et al., “Classification of Rest State EEG in Patients with Schizophrenia or Depression,” Computer Engineerding and Applications, pp. 1-13. [Google Scholar]
  12. S. Alhagry, A. A. Fahmy, and R. A. El-Khoribi, “Emotion recognition based on EEG using LSTM recurrent neural network,” Emotion, vol. 8, no. 10, pp. 355-358, 2017. [Google Scholar]
  13. X. Zhang, L. Yao, S. S. Kanhere, Y. Liu, T. Gu, and K. Chen, “Mindid: Person identification from brain waves through attention-based recurrent neural network,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 3, pp. 1-23, 2018. [Google Scholar]
  14. S. Zhang, H. Tong, J. Xu, and R. Maciejewski, “Graph convolutional networks: a comprehensive review,” Computational Social Networks, vol. 6, no. 1, pp. 1-23, 2019. [CrossRef] [Google Scholar]
  15. Y. Yin, X. Zheng, B. Hu, Y. Zhang, and X. Cui, “EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM,” Applied Soft Computing, vol. 100, p. 106954, 2021. [CrossRef] [Google Scholar]
  16. S. Tang et al., “Automated Seizure Detection and Seizure Type Classification From Electroencephalography With a Graph Neural Network and Self-Supervised Pre-Training,” arXiv preprint arXiv:.08336, 2021. [Google Scholar]
  17. X. Lun, S. Jia, Y. Hou, Y. Shi, and Y. Li, “GCNs-net: a graph convolutional neural network approach for decoding time-resolved eeg motor imagery signals,” arXiv preprint arXiv:.08924, 2020. [Google Scholar]
  18. W. Bleidorn, C. J. Hopwood, and R. E. Lucas, “Life events and personality trait change,” Journal of personality, vol. 86, no. 1, pp. 83-96, 2018. [CrossRef] [Google Scholar]
  19. L. Hu and Z. Zhang, EEG Signal Processing and Feature Extraction. Springer, 2019. [CrossRef] [Google Scholar]
  20. F. Lotte and M. Congedo, “EEG feature extraction,” Brain–Computer Interfaces 1: Foundations Methods, pp. 127-143, 2016. [CrossRef] [Google Scholar]
  21. Y. Kim, “Convolutional Neural Networks for Sentence Classification,” Eprint Arxiv, 2014. [Google Scholar]
  22. K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A search space odyssey,” IEEE transactions on neural networks and learning systems, vol. 28, no. 10, pp. 2222-2232, 2016. [Google Scholar]
  23. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998. [CrossRef] [Google Scholar]
  24. A. Paszke et al., “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems, vol. 32, 2019. [Google Scholar]
  25. PyTorch:https://github.com/pytorch/pytorch. [Google Scholar]

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