References
- S. Kim, H. Yang, N. Nguyen, S. Prabhakar and S. Lee, “WeDea: A New EEG-Based Framework for Emotion Recognition,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 1, 2022, pp. 264-275. Doi: 10.1109/jbhi.2021.3091187.
- N. Salankar, P. Mishra and L. Garg, “Emotion Recognition From EEG Signals Using Empirical Mode Decomposition And Second-Order Difference Plot,” Biomedical Signal Processing and Control, vol. 65, 2021, p. 102389. Doi: 10.1016/j.bspc.2020.102389.
- A. Subasi, T. Tuncer, S. Dogan, D. Tanko and U. Sakoglu, “EEG-Based Emotion Recognition Using Tunable Q Wavelet Transform And Rotation Forest Ensemble Classifier,” Biomedical Signal Processing and Control, vol. 68, 2021, p. 102648. Doi: 10.1016/j.bspc.2021.102648.
- P.V. and A. Bhattacharyya, “Human Emotion Recognition Based On Time–Frequency Analysis Of Multivariate EEG Signal”, Knowledge- Based Systems, vol. 238, 2022, p. 107867. Doi: 10.1016/j.knosys.2021.107867.
- J. Wang and M. Wang, “Review Of The Emotional Feature Extraction And Classification Using EEG Signals,” Cognitive Robotics, vol. 1, 2021, pp. 29-40. Doi: 10.1016/j.cogr.2021.04.001.
- X. Zhou, X. Tang and R. Zhang, “Impact Of Green Finance On Economic Development And Environmental Quality: A Study Based On Provincial Panel Data From China,” Environmental Science and Pollution Research, vol. 27, no. 16, 2020, pp. 19915-19932. Doi: 10.1007/s11356-020-08383-2.
- N. Garcia, B. Renoust and Y. Nakashima, “ContextNet: representation and exploration for painting classification and retrieval in context,” International Journal of Multimedia Information Retrieval, vol. 9, no. 1, 2019, pp. 17-30. Doi: 10.1007/s13735-019-00189-4.
- F. Zhou, Q. Yang, K. Zhang, G. Trajcevski, T. Zhong and A. Khokhar, “Reinforced Spatiotemporal Attentive Graph Neural Networks for Traffic Forecasting,” IEEE Internet of Things Journal, vol. 7, no. 7, 2020, pp. 6414-6428. Doi: 10.1109/jiot.2020.2974494.
- A. Chowdhury and D. De, “Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-Glowworm Swarm Optimization-K-means algorithm,” Ad Hoc Networks, vol. 122, 2021, p. 102660. Doi: 10.1016/j.adhoc.2021.102660.
- M. Khateeb, S. Anwar and M. Alnowami, “Multi-Domain Feature Fusion for Emotion Classification Using DEAP Dataset,” IEEE Access., vol. 9, 2021, pp. 12134-12142. Doi: 10.1109/access.2021.3051281.
- 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, 2021, p. 106954. Doi: 10.1016/j.asoc.2020.106954.
- V. Dissanayake, S. Seneviratne, R. Rana, E. Wen, T. Kaluarachchi and S. Nanayakkara, “SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning”, IEEE Access, vol. 10, 2022, pp. 18105-18120. Doi: 10.1109/access.2022.3149509.
- K. Yang, B. Tag, Y. Gu, C. Wang, T. Dingler, G. Wadley, J. Goncalves. “Mobile emotion recognition via multiple physiological signals using convolution-augmented transformer”. InProceedings of the 2022 International Conference on Multimedia Retrieval. pp. 562-570 2022. Doi: 10.1145/3512527.3531385
- S. Koelstra, C. Muhl, M. Soleymani, J.S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, and I. Patras, “DEAP: A Database for Emotion Analysis Using Physiological Signals,” IEEE Transactions on Affective Computing, vol. 3, no. 1, 2012, pp. 18-31. Doi: 10.1109/t-affc.2011.15.
- C.Y. Park, N. Cha, S. Kang, A. Kim, A.H. Khandoker, L. Hadjileontiadis, A. Oh, Y. Jeong, and U. Lee, “K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations,” Scientific Data, vol. 7, no. 1, 2020. Doi: 10.1038/s41597-020-00630-y.
