Open Access

EEG Based Emotion Analysis Using Reinforced Spatio-Temporal Attentive Graph Neural and Contextnet Techniques

 and   
Sep 12, 2024

Cite
Download Cover

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar