Temporal shift residual network for EEG-based emotion recognition: A 3D feature image sequence approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17142-7.pdf
Reference46 articles.
1. Doma V, Pirouz M (2020) A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals. J Big Data 7(1):1–21. https://doi.org/10.1186/s40537-020-00289-7
2. Pan C, Shi C, Mu H, Li J, Gao X (2020) EEG-based emotion recognition using logistic regression with Gaussian kernel and Laplacian prior and investigation of critical frequency bands. Appl Sci 10(5):1619. https://doi.org/10.3390/app10051619
3. Egger M, Ley M, Hanke S (2019) Emotion recognition from physiological signal analysis: A review. Electronic Notes Theoret Comput Sci 343:35–55. https://doi.org/10.1016/j.entcs.2019.04.009
4. Zheng WL, Lu BL (2015) Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Autonomous Mental Dev 7(3):162–175. https://doi.org/10.1109/TAMD.2015.2431497
5. Bhatti AM, Majid M, Anwar SM, Khan B (2016) Human emotion recognition and analysis in response to audio music using brain signals. Comput Human Behavior 65:267–275. https://doi.org/10.1016/j.chb.2016.08.029
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