{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T12:18:21Z","timestamp":1781093901107,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819543779","type":"print"},{"value":"9789819543786","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-4378-6_1","type":"book-chapter","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:52:40Z","timestamp":1762789960000},"page":"3-17","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MVGT: A Multi-view Graph Transformer Based on\u00a0Spatial Relations for\u00a0EEG Emotion Recognition"],"prefix":"10.1007","author":[{"given":"Yan-Jie","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Hong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya-Min","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,9]]},"reference":[{"issue":"3","key":"1_CR1","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1109\/TAFFC.2017.2714671","volume":"10","author":"SM Alarcao","year":"2017","unstructured":"Alarcao, S.M., Fonseca, M.J.: Emotions recognition using EEG signals: a survey. IEEE Trans. Affect. Comput. 10(3), 374\u2013393 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"3","key":"1_CR2","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.conb.2012.12.012","volume":"23","author":"LF Barrett","year":"2013","unstructured":"Barrett, L.F., Satpute, A.B.: Large-scale brain networks in affective and social neuroscience: towards an integrative functional architecture of the brain. Curr. Opin. Neurobiol. 23(3), 361\u2013372 (2013)","journal-title":"Curr. Opin. Neurobiol."},{"key":"1_CR3","unstructured":"Cai, T., Luo, S., Xu, K., He, D., Liu, T.Y., Wang, L.: Graphnorm: a principled approach to accelerating graph neural network training. In: International Conference on Machine Learning, pp. 1204\u20131215. PMLR (2021)"},{"issue":"3","key":"1_CR4","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab0ab5","volume":"16","author":"A Craik","year":"2019","unstructured":"Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16(3), 031001 (2019)","journal-title":"J. Neural Eng."},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Ding, Y., Robinson, N., Tong, C., Zeng, Q., Guan, C.: Lggnet: learning from local-global-graph representations for brain\u2013computer interface. IEEE Trans. Neural Netw. Learn. Syst. 1\u201314 (2023). https:\/\/doi.org\/10.1109\/TNNLS.2023.3236635","DOI":"10.1109\/TNNLS.2023.3236635"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Duan, R.N., Zhu, J.Y., Lu, B.L.: Differential entropy feature for EEG-based emotion classification. In: 2013 6th International IEEE\/EMBS Conference on Neural Engineering (NER), pp. 81\u201384. IEEE (2013)","DOI":"10.1109\/NER.2013.6695876"},{"key":"1_CR7","doi-asserted-by":"publisher","first-page":"35080","DOI":"10.3389\/fpsyg.2012.00428","volume":"3","author":"RH Grabner","year":"2012","unstructured":"Grabner, R.H., De Smedt, B.: Oscillatory EEG correlates of arithmetic strategies: a training study. Front. Psychol. 3, 35080 (2012)","journal-title":"Front. Psychol."},{"issue":"2","key":"1_CR8","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","volume":"4","author":"K Hornik","year":"1991","unstructured":"Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251\u2013257 (1991)","journal-title":"Neural Netw."},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Jiang, W.B., Yan, X., Zheng, W.L., Lu, B.L.: Elastic graph transformer networks for EEG-based emotion recognition. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10096511"},{"issue":"7873","key":"1_CR10","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper, J., et al.: Highly accurate protein structure prediction with alphafold. Nature 596(7873), 583\u2013589 (2021)","journal-title":"Nature"},{"issue":"2","key":"1_CR11","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1016\/j.neuroimage.2008.03.059","volume":"42","author":"H Kober","year":"2008","unstructured":"Kober, H., Barrett, L.F., Joseph, J., Bliss-Moreau, E., Lindquist, K., Wager, T.D.: Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies. Neuroimage 42(2), 998\u20131031 (2008)","journal-title":"Neuroimage"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, Y., Lu, B.L.: A multi-domain adaptive graph convolutional network for EEG-based emotion recognition. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 5565\u20135573 (2021)","DOI":"10.1145\/3474085.3475697"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, Y., Zheng, W.L., Lu, B.L.: A multi-view spectral-spatial-temporal masked autoencoder for decoding emotions with self-supervised learning. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 6\u201314 (2022)","DOI":"10.1145\/3503161.3548243"},{"issue":"2","key":"1_CR14","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1109\/TCDS.2020.2999337","volume":"13","author":"Y Li","year":"2020","unstructured":"Li, Y., et al.: A novel bi-hemispheric discrepancy model for EEG emotion recognition. IEEE Trans. Cogn. Dev. Syst. 13(2), 354\u2013367 (2020)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"2","key":"1_CR15","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1109\/TAFFC.2019.2922912","volume":"13","author":"Y Li","year":"2022","unstructured":"Li, Y., Zheng, W., Wang, L., Zong, Y., Cui, Z.: From regional to global brain: a novel hierarchical spatial-temporal neural network model for EEG emotion recognition. IEEE Trans. Affect. Comput. 13(2), 568\u2013578 (2022). https:\/\/doi.org\/10.1109\/TAFFC.2019.2922912","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"2","key":"1_CR16","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TAFFC.2018.2885474","volume":"12","author":"Y Li","year":"2018","unstructured":"Li, Y., Zheng, W., Zong, Y., Cui, Z., Zhang, T., Zhou, X.: A bi-hemisphere domain adversarial neural network model for EEG emotion recognition. IEEE Trans. Affect. Comput. 12(2), 494\u2013504 (2018)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"1_CR17","unstructured":"Liu, Y., et al.: itransformer: Inverted transformers are effective for time series forecasting. In: The Twelfth International Conference on Learning Representations (2024). https:\/\/openreview.net\/forum?id=JePfAI8fah"},{"key":"1_CR18","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=Bkg6RiCqY7"},{"key":"1_CR19","unstructured":"Mauss, I.B., Robinson, M.D.: Measures of emotion: a reviews. In: Cognition and Emotion, pp. 109\u2013137 (2010)"},{"key":"1_CR20","unstructured":"M\u00fcller, L., Galkin, M., Morris, C., Ramp\u00e1\u0161ek, L.: Attending to graph transformers. Trans. Mach. Learn. Res. (2024). https:\/\/openreview.net\/forum?id=HhbqHBBrfZ"},{"key":"1_CR21","unstructured":"Niemic, C.: Studies of emotion: a theoretical and empirical review of psychophysiological studies of emotion. J. Undergraduate Res. (2004)"},{"issue":"4","key":"1_CR22","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1080\/02699930126048","volume":"15","author":"LA Schmidt","year":"2001","unstructured":"Schmidt, L.A., Trainor, L.J.: Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cogn. Emotion 15(4), 487\u2013500 (2001)","journal-title":"Cogn. Emotion"},{"key":"1_CR23","unstructured":"Shi, Y., et al.: Benchmarking graphormer on large-scale molecular modeling datasets. arXiv preprint arXiv:2203.04810 (2022)"},{"key":"1_CR24","unstructured":"Shuaibi, M., et al.: Rotation invariant graph neural networks using spin convolutions. arXiv preprint arXiv:2106.09575 (2021)"},{"issue":"3","key":"1_CR25","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","volume":"11","author":"T Song","year":"2018","unstructured":"Song, T., Zheng, W., Song, P., Cui, Z.: EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput. 11(3), 532\u2013541 (2018)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"1_CR26","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"1_CR27","unstructured":"Xiong, R., et al.: On layer normalization in the transformer architecture (2020). https:\/\/openreview.net\/forum?id=B1x8anVFPr"},{"issue":"3","key":"1_CR28","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","volume":"49","author":"WL Zheng","year":"2018","unstructured":"Zheng, W.L., Liu, W., Lu, Y., Lu, B.L., Cichocki, A.: Emotionmeter: a multimodal framework for recognizing human emotions. IEEE Trans. Cybern. 49(3), 1110\u20131122 (2018)","journal-title":"IEEE Trans. Cybern."},{"issue":"3","key":"1_CR29","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","volume":"7","author":"WL Zheng","year":"2015","unstructured":"Zheng, W.L., Lu, B.L.: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. 7(3), 162\u2013175 (2015)","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"issue":"3","key":"1_CR30","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1109\/TAFFC.2020.2994159","volume":"13","author":"P Zhong","year":"2020","unstructured":"Zhong, P., Wang, D., Miao, C.: EEG-based emotion recognition using regularized graph neural networks. IEEE Trans. Affect. Comput. 13(3), 1290\u20131301 (2020)","journal-title":"IEEE Trans. Affect. Comput."}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4378-6_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T05:58:52Z","timestamp":1767938332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4378-6_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,9]]},"ISBN":["9789819543779","9789819543786"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4378-6_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,9]]},"assertion":[{"value":"9 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Okinawa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2025.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}