{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T00:45:07Z","timestamp":1770425107374,"version":"3.49.0"},"reference-count":45,"publisher":"National Library of Serbia","issue":"3","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>Effective emotion recognition based on electroencephalography (EEG) is crucial for the development of Brain-Computer Interface (BCI). Neuroscientific studies highlight the importance of localized brain activity analysis for understanding emotional states. However, existing deep learning methods often fail to extract spatio-temporal features of EEG signals adequately. Accordingly, we propose a novel spatio-temporal graph neural network, MSL-TGNN, by integrating local and global brain information. A multi-scale temporal learner is designed to extract EEG temporal dependencies. And a brain region learning block and an extended global graph attention network are introduced to explore the spatial features. Specifically, the brain region learning block aggregates local channel information, whereas the extended global graph attention network can effectively capture nonlinear dependencies among regions to extract global brain information. We conducted subject-dependent and subject-independent experiments on the DEAP dataset, and the results indicate that our proposed model outperforms compared to state-of-theart methods.<\/jats:p>","DOI":"10.2298\/csis250215053w","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T07:14:31Z","timestamp":1747984471000},"page":"971-989","source":"Crossref","is-referenced-by-count":1,"title":["A spatio-temporal graph neural network for EEG emotion recognition based on regional and global brain"],"prefix":"10.2298","volume":"22","author":[{"given":"Xiaoliang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China + Sanya Research Institute, Hunan University of Science and Technology, Sanya, China"}]},{"given":"Chuncao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China + Sanya Research Institute, Hunan University of Science and Technology, Sanya, China"}]},{"given":"Yuzhen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China + Sanya Research Institute, Hunan University of Science and Technology, Sanya, China"}]},{"given":"Wei","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China + Sanya Research Institute, Hunan University of Science and Technology, Sanya, China"}]},{"given":"Kuanching","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China + Sanya Research Institute, Hunan University of Science and Technology, Sanya, China"}]},{"given":"Aneta","family":"Poniszewska-Maranda","sequence":"additional","affiliation":[{"name":"Institute of Information Technology, Lodz University of Technology, Lodz, Poland"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Abgeena, A., Garg, S.: A novel convolution bi-directional gated recurrent unit neural network for emotion recognition in multichannel electroencephalogram signals. 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Advances in neural information processing systems 30 (2017)"},{"key":"ref35","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. stat 1050(20), 10-48550 (2017)"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"Wang, Y., Shi, Y., He, Z., Chen, Z., Zhou, Y.: Combining temporal and spatial attention for seizure prediction. Health Information Science and Systems 11(1), 38 (2023)","DOI":"10.1007\/s13755-023-00239-6"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"Wang, Z.,Wang, Y., Zhang, J., Hu, C., Yin, Z., Song, Y.: Spatial-temporal feature fusion neural network for eeg-based emotion recognition. IEEE Transactions on Instrumentation and Measurement 71, 1-12 (2022)","DOI":"10.1109\/TIM.2022.3165280"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wu, Q., Fu, Y., Chen, X.: Continuous convolutional neural network with 3d input for eeg-based emotion recognition. 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Biomedical Signal Processing and Control 87, 105487 (2024)","DOI":"10.1016\/j.bspc.2023.105487"},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"Zheng, W.L., Zhu, J.Y., Peng, Y., Lu, B.L.: Eeg-based emotion classification using deep belief networks. In: 2014 IEEE international conference on multimedia and expo (ICME). pp. 1-6. IEEE (2014)","DOI":"10.1109\/ICME.2014.6890166"},{"key":"ref45","doi-asserted-by":"crossref","unstructured":"Zhou, S., Li, K., Chen, Y., Yang, C., Liang, W., Zomaya, A.Y.: Trustbcfl: Mitigating data bias in iot through blockchain-enabled federated learning. 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