{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T06:48:32Z","timestamp":1758350912192,"version":"3.44.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032051400"},{"type":"electronic","value":"9783032051417"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-05141-7_7","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:15:47Z","timestamp":1758269747000},"page":"64-73","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Modal Contrastive Learning for\u00a0Emotion Recognition: Aligning ECG with\u00a0EEG-Derived Features"],"prefix":"10.1007","author":[{"given":"Yi","family":"Wu","sequence":"first","affiliation":[]},{"given":"Yuhang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jiahao","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Jiaji","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lin","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"7_CR1","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s11055-005-0170-6","volume":"36","author":"L Aftanas","year":"2006","unstructured":"Aftanas, L., Reva, N., Savotina, L., Makhnev, V.: Neurophysiological correlates of induced discrete emotions in humans: an individually oriented analysis. Neurosci. Behav. Physiol. 36, 119\u2013130 (2006)","journal-title":"Neurosci. Behav. Physiol."},{"doi-asserted-by":"crossref","unstructured":"Billones, R.K.C., et al.: Cardiac and brain activity correlation analysis using electrocardiogram and electroencephalogram signals. In: 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp.\u00a01\u20136. IEEE (2018)","key":"7_CR2","DOI":"10.1109\/HNICEM.2018.8666392"},{"issue":"3","key":"7_CR3","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.jsmc.2007.05.002","volume":"2","author":"RE Dahl","year":"2007","unstructured":"Dahl, R.E., Harvey, A.G.: Sleep in children and adolescents with behavioral and emotional disorders. Sleep Med. Clin. 2(3), 501\u2013511 (2007)","journal-title":"Sleep Med. Clin."},{"doi-asserted-by":"publisher","unstructured":"Ding, Z., et al.: Cross-modality cardiac insight transfer: a contrastive learning approach to enrich ECG with CMR features. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15003, pp. 109\u2013119. Springer, Marrakesh (2024). https:\/\/doi.org\/10.1007\/978-3-031-72384-1_11","key":"7_CR4","DOI":"10.1007\/978-3-031-72384-1_11"},{"key":"7_CR5","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.entcs.2019.04.009","volume":"343","author":"M Egger","year":"2019","unstructured":"Egger, M., Ley, M., Hanke, S.: Emotion recognition from physiological signal analysis: a review. Electron. Notes Theor. Comput. Sci. 343, 35\u201355 (2019)","journal-title":"Electron. Notes Theor. Comput. Sci."},{"doi-asserted-by":"crossref","unstructured":"Eldele, E., et al.: Time-series representation learning via temporal and contextual contrasting. arXiv preprint arXiv:2106.14112 (2021)","key":"7_CR6","DOI":"10.24963\/ijcai.2021\/324"},{"doi-asserted-by":"publisher","unstructured":"Fan, X., Xu, P., Zhao, Q., Hao, C., Zhao, Z., Wang, Z.: A domain adaption approach for EEG-based automated seizure classification with temporal-spatial-spectral attention. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15005, pp. 14\u201324. Springer, Marrakesh (2024). https:\/\/doi.org\/10.1007\/978-3-031-72086-4_2","key":"7_CR7","DOI":"10.1007\/978-3-031-72086-4_2"},{"issue":"3","key":"7_CR8","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1109\/TAFFC.2018.2890636","volume":"12","author":"B Garc\u00eda-Mart\u00ednez","year":"2019","unstructured":"Garc\u00eda-Mart\u00ednez, B., Martinez-Rodrigo, A., Alcaraz, R., Fern\u00e1ndez-Caballero, A.: A review on nonlinear methods using electroencephalographic recordings for emotion recognition. IEEE Trans. Affect. Comput. 12(3), 801\u2013820 (2019)","journal-title":"IEEE Trans. Affect. Comput."},{"doi-asserted-by":"crossref","unstructured":"Jin, M., Du, C., He, H., Cai, T., Li, J.: PGCN: pyramidal graph convolutional network for EEG emotion recognition. IEEE Trans. Multimedia (2024)","key":"7_CR9","DOI":"10.1109\/TMM.2024.3385676"},{"issue":"18","key":"7_CR10","doi-asserted-by":"publisher","first-page":"27269","DOI":"10.1007\/s11042-023-14489-9","volume":"82","author":"K Kamble","year":"2023","unstructured":"Kamble, K., Sengupta, J.: A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals. Multimedia Tools Appl. 82(18), 27269\u201327304 (2023)","journal-title":"Multimedia Tools Appl."},{"issue":"1","key":"7_CR11","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/JBHI.2017.2688239","volume":"22","author":"S Katsigiannis","year":"2017","unstructured":"Katsigiannis, S., Ramzan, N.: Dreamer: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J. Biomed. Health Inform. 22(1), 98\u2013107 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"doi-asserted-by":"publisher","unstructured":"Kumar, V., et al.: mulEEG: a multi-view representation learning on EEG signals. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13433, pp. 398\u2013407. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_38","key":"7_CR12","DOI":"10.1007\/978-3-031-16437-8_38"},{"issue":"5","key":"7_CR13","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J. Neural Eng. 15(5), 056013 (2018)","journal-title":"J. Neural Eng."},{"unstructured":"Liu, Y., et al.: itransformer: inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625 (2023)","key":"7_CR14"},{"doi-asserted-by":"crossref","unstructured":"Lucey, P., et al.: Automatically detecting pain in video through facial action units. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41(3), 664\u2013674 (2010)","key":"7_CR15","DOI":"10.1109\/TSMCB.2010.2082525"},{"issue":"2","key":"7_CR16","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1109\/TAFFC.2018.2884461","volume":"12","author":"JA Miranda-Correa","year":"2018","unstructured":"Miranda-Correa, J.A., Abadi, M.K., Sebe, N., Patras, I.: Amigos: a dataset for affect, personality and mood research on individuals and groups. IEEE Trans. Affect. Comput. 12(2), 479\u2013493 (2018)","journal-title":"IEEE Trans. Affect. Comput."},{"unstructured":"Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)","key":"7_CR17"},{"issue":"2","key":"7_CR18","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1111\/j.1469-8986.2007.00497.x","volume":"44","author":"D Sammler","year":"2007","unstructured":"Sammler, D., Grigutsch, M., Fritz, T., Koelsch, S.: Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology 44(2), 293\u2013304 (2007)","journal-title":"Psychophysiology"},{"issue":"3","key":"7_CR19","doi-asserted-by":"publisher","first-page":"1541","DOI":"10.1109\/TAFFC.2020.3014842","volume":"13","author":"P Sarkar","year":"2020","unstructured":"Sarkar, P., Etemad, A.: Self-supervised ECG representation learning for emotion recognition. IEEE Trans. Affect. Comput. 13(3), 1541\u20131554 (2020)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TNSRE.2022.3230250","volume":"31","author":"Y Song","year":"2022","unstructured":"Song, Y., Zheng, Q., Liu, B., Gao, X.: EEG conformer: convolutional transformer for EEG decoding and visualization. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 710\u2013719 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"3","key":"7_CR21","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TBCAS.2021.3090786","volume":"15","author":"J Sun","year":"2021","unstructured":"Sun, J., Han, J., Wang, Y., Liu, P.: Memristor-based neural network circuit of emotion congruent memory with mental fatigue and emotion inhibition. IEEE Trans. Biomed. Circuits Syst. 15(3), 606\u2013616 (2021)","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"issue":"1","key":"7_CR22","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1109\/TAFFC.2020.3025777","volume":"14","author":"W Tao","year":"2020","unstructured":"Tao, W., et al.: EEG-based emotion recognition via channel-wise attention and self attention. IEEE Trans. Affect. Comput. 14(1), 382\u2013393 (2020)","journal-title":"IEEE Trans. Affect. Comput."},{"doi-asserted-by":"crossref","unstructured":"Zhang, D., Yuan, Z., Chen, J., Chen, K., Yang, Y.: Brant-x: a unified physiological signal alignment framework. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4155\u20134166 (2024)","key":"7_CR23","DOI":"10.1145\/3637528.3671953"},{"key":"7_CR24","first-page":"3988","volume":"35","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Zhao, Z., Tsiligkaridis, T., Zitnik, M.: Self-supervised contrastive pre-training for time series via time-frequency consistency. Adv. Neural. Inf. Process. Syst. 35, 3988\u20134003 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"doi-asserted-by":"publisher","unstructured":"Zhao, Y., Gu, J.: Feature fusion based on mutual-cross-attention mechanism for EEG emotion recognition. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15011, pp. 276\u2013285. Springer, Marrakesh (2024). https:\/\/doi.org\/10.1007\/978-3-031-72120-5_26","key":"7_CR25","DOI":"10.1007\/978-3-031-72120-5_26"},{"doi-asserted-by":"publisher","unstructured":"Zheng, C., Shao, W., Zhang, D., Zhu, Q.: Prior-driven dynamic brain networks for multi-modal emotion recognition. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14227, pp. 389\u2013398. Springer, Vancouver (2023). https:\/\/doi.org\/10.1007\/978-3-031-43993-3_38","key":"7_CR26","DOI":"10.1007\/978-3-031-43993-3_38"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05141-7_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:15:53Z","timestamp":1758269753000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05141-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032051400","9783032051417"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05141-7_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}