{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T20:47:53Z","timestamp":1754599673309,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981373"},{"type":"electronic","value":"9789819981380"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8138-0_32","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T10:02:23Z","timestamp":1700906543000},"page":"402-414","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DAformer: Transformer with\u00a0Domain Adversarial Adaptation for\u00a0EEG-Based Emotion Recognition with\u00a0Live-Oil Paintings"],"prefix":"10.1007","author":[{"given":"Zhong-Wei","family":"Jin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia-Wen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Long","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bao-Liang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"issue":"3","key":"32_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":"32_CR2","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","volume":"10","author":"W-L Zheng","year":"2019","unstructured":"Zheng, W.-L., Zhu, J.-Y., Lu, B.-L.: Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans. Affect. Comput. 10(3), 417\u2013429 (2019)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Schaaff, K., Schultz, T.: Towards emotion recognition from electroencephalographic signals. In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1-6. IEEE (2009)","DOI":"10.1109\/ACII.2009.5349316"},{"issue":"1","key":"32_CR4","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","volume":"3","author":"S Koelstra","year":"2011","unstructured":"Koelstra, S., Muhl, C., Soleymani, M.: Deap: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18\u201331 (2011)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"7","key":"32_CR5","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TBME.2010.2048568","volume":"57","author":"YP Lin","year":"2010","unstructured":"Lin, Y.P., et al.: EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57(7), 1798\u20131806 (2010)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Luo, S., Lan, Y.T., Peng, D., Li, Z., Zheng, W.L., Lu, B.L.: Multimodal emotion recognition in response to oil paintings. In: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4167-4170. IEEE (2022)","DOI":"10.1109\/EMBC48229.2022.9871630"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Lan, Y.T., Li, Z.C., Peng, D., Zheng, W.L., Lu, B.L.: Identifying artistic expertise difference in emotion recognition in response to oil paintings. In: 11th International IEEE\/EMBS Conference on Neural Engineering (NER), pp. 1\u20134. IEEE (2023)","DOI":"10.1109\/NER52421.2023.10123777"},{"key":"32_CR8","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","volume":"79","author":"S Ben-David","year":"2010","unstructured":"Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Mach. Learn. 79, 151\u2013175 (2010)","journal-title":"Mach. Learn."},{"key":"32_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/978-3-030-04221-9_36","volume-title":"Neural Information Processing","author":"H Li","year":"2018","unstructured":"Li, H., Jin, Y.-M., Zheng, W.-L., Lu, B.-L.: Cross-subject emotion recognition using deep adaptation networks. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11305, pp. 403\u2013413. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-04221-9_36"},{"issue":"4","key":"32_CR10","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.1109\/TNNLS.2020.2988928","volume":"32","author":"Y Zhu","year":"2021","unstructured":"Zhu, Y., et al.: Deep subdomain adaptation network for image classification. IEEE Trans. Neural Networks Learn. Syst. 32(4), 1713\u20131722 (2021)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jiang, W. B., Li, R., Lu, B.L.: Emotion transformer fusion: complementary representation properties of EEG and eye movements on recognizing anger and surprise. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1575\u20131578. IEEE (2021)","DOI":"10.1109\/BIBM52615.2021.9669556"},{"key":"32_CR12","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/978-981-99-1642-9_34","volume-title":"ICONIP 2022","author":"R Li","year":"2022","unstructured":"Li, R., Wang, Y., Lu, B.L.: Measuring decision confidence levels from EEG using a spectral-spatial-temporal adaptive graph convolutional neural network. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) ICONIP 2022. LNCS, pp. 395\u2013406. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-981-99-1642-9_34"},{"issue":"7","key":"32_CR13","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1080\/02699930903274322","volume":"24","author":"A Schaefer","year":"2010","unstructured":"Schaefer, A., et al.: Assessing the effectiveness of a large database of emotion-eliciting films: a new tool for emotion researchers. Cogn. Emot. 24(7), 1153\u20131172 (2010)","journal-title":"Cogn. Emot."},{"key":"32_CR14","unstructured":"Zheng, W.L., Lu, B.L.: Personalizing EEG-based affective models with transfer learning. In: International Joint Conference on Artificial Intelligence, pp. 2732\u20132738. AAAI Press, New York (2016)"},{"issue":"12","key":"32_CR15","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1068\/p6747","volume":"39","author":"R Batt","year":"2010","unstructured":"Batt, R., Palmiero, M., Nakatani, C., van Leeuwen, C.: Style and spectral power: processing of abstract and representational art in artists and non-artists. Perception 39(12), 1659\u20131671 (2010)","journal-title":"Perception"},{"issue":"2","key":"32_CR16","doi-asserted-by":"publisher","first-page":"207","DOI":"10.2190\/EM.28.2.f","volume":"28","author":"A Chatterjee","year":"2010","unstructured":"Chatterjee, A., Widick, P., Sternschein, R., Smith, W.B., Bromberger, B.: The assessment of art attributes. Empir. Stud. Arts 28(2), 207\u2013222 (2010)","journal-title":"Empir. Stud. Arts"},{"key":"32_CR17","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"32_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"B Sun","year":"2016","unstructured":"Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443\u2013450. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_35"},{"issue":"1","key":"32_CR19","first-page":"2030","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030\u20132096 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Wang, J., Feng, W., Chen, Y., Yu, H., Huang, M., Yu, P.S.: Visual domain adaptation with manifold embedded distribution alignment. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 402-410 (2018)","DOI":"10.1145\/3240508.3240512"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8138-0_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:34:51Z","timestamp":1710351291000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8138-0_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981373","9789819981380"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8138-0_32","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","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":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}