{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:56:54Z","timestamp":1776884214270,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["GZB20230960;2023M730873"],"award-info":[{"award-number":["GZB20230960;2023M730873"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306089"],"award-info":[{"award-number":["62306089"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["RCBS20231211090800003"],"award-info":[{"award-number":["RCBS20231211090800003"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3681645","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:41Z","timestamp":1729925981000},"page":"340-349","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["EEG-MACS: Manifold Attention and Confidence Stratification for EEG-based Cross-Center Brain Disease Diagnosis under Unreliable Annotations"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8574-0857","authenticated-orcid":false,"given":"Zhenxi","family":"Song","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8188-9529","authenticated-orcid":false,"given":"Ruihan","family":"Qin","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2219-901X","authenticated-orcid":false,"given":"Huixia","family":"Ren","sequence":"additional","affiliation":[{"name":"Shenzhen People's Hospital, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1749-2975","authenticated-orcid":false,"given":"Zhen","family":"Liang","sequence":"additional","affiliation":[{"name":"Shenzhen University, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2614-6118","authenticated-orcid":false,"given":"Yi","family":"Guo","sequence":"additional","affiliation":[{"name":"Shenzhen People's Hospital & Shenzhen Bay Laboratory, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3895-5510","authenticated-orcid":false,"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7992-7965","authenticated-orcid":false,"given":"Zhiguo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parkreldis.2020.08.001"},{"key":"e_1_3_2_1_2_1","volume-title":"Resting-state EEG measures cognitive impairment in Parkinson's disease. npj Parkinson's Disease","author":"Anjum Md Fahim","year":"2024","unstructured":"Md Fahim Anjum, Arturo I Espinoza, Rachel C Cole, Arun Singh, Patrick May, Ergun Y Uc, Soura Dasgupta, and Nandakumar S Narayanan. 2024. Resting-state EEG measures cognitive impairment in Parkinson's disease. npj Parkinson's Disease, Vol. 10, 1 (2024), 6."},{"key":"e_1_3_2_1_3_1","volume-title":"Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems (NeurIPS).","author":"Berthelot David","year":"2019","unstructured":"David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. 2019. Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_4_1","volume-title":"Sliced-Wasserstein on Symmetric Positive Definite Matrices for M\/EEG Signals. In International Conference on Machine Learning (ICML).","author":"Bonet Cl\u00e9ment","year":"2023","unstructured":"Cl\u00e9ment Bonet, Benoit Mal'ezieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, and Nicolas Courty. 2023. Sliced-Wasserstein on Symmetric Positive Definite Matrices for M\/EEG Signals. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599426"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2017.11.023"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3292452"},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Chen Ting","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-020-09473-9"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118772"},{"key":"e_1_3_2_1_11_1","article-title":"Parkinson disease-associated cognitive impairment (primer)","volume":"7","author":"Dag Aarsland","year":"2021","unstructured":"Aarsland Dag, Batzu Lucia, Glenda M Halliday, Gert J Geurtsen, Clive Ballard, Chaudhuri K Ray, and Daniel Weintraub. 2021. Parkinson disease-associated cognitive impairment (primer). Nature Reviews: Disease Primers, Vol. 7, 1 (2021).","journal-title":"Nature Reviews: Disease Primers"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00714-5"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/324"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3308189"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02525-0"},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Heinrichs Florian","year":"2023","unstructured":"Florian Heinrichs, Mavin Heim, and Corinna Weber. 2023. Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_17_1","unstructured":"Dan Hendrycks Mantas Mazeika Duncan Wilson and Kevin Gimpel. 2018. Using trusted data to train deep networks on labels corrupted by severe noise. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10866"},{"key":"e_1_3_2_1_19_1","volume-title":"International conference on machine learning (ICML).","author":"Jiang Lu","year":"2020","unstructured":"Lu Jiang, Di Huang, Mason Liu, and Weilong Yang. 2020. Beyond synthetic noise: Deep learning on controlled noisy labels. In International conference on machine learning (ICML)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Derek K Jones Val Lowe Jonathan Graff-Radford Hugo Botha Leland Barnard D Wiepert Matthew C Murphy Melissa Murray Matthew Senjem Jeffrey Gunter et al. 2022. A computational model of neurodegeneration in Alzheimer's disease. Nature communications Vol. 13 1 (2022) 1643.","DOI":"10.1038\/s41467-022-29047-4"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3172108"},{"key":"e_1_3_2_1_22_1","unstructured":"Prannay Khosla Piotr Teterwak Chen Wang Aaron Sarna Yonglong Tian Phillip Isola Aaron Maschinot Dilip Krishnan and Ce Liu. 2020. Supervised contrastive learning. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-021-00269-y"},{"key":"e_1_3_2_1_24_1","unstructured":"Reinmar Kobler Jun-ichiro Hirayama Qibin Zhao and Motoaki Kawanabe. 2022. SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_25_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Li Junnan","unstructured":"Junnan Li, Richard Socher, and Steven C. H. Hoi. 2020. DivideMix: Learning with Noisy Labels as Semi-supervised Learning. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00041"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120116"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/450"},{"key":"e_1_3_2_1_29_1","unstructured":"Eran Malach and Shai Shalev-Shwartz. 2017. Decoupling \"when to update\" from \"how to update\". In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00654"},{"key":"e_1_3_2_1_31_1","unstructured":"Yue-Ting Pan Jing-Lun Chou and Chun-Shu Wei. 2022. MAtt: a manifold attention network for EEG decoding. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_32_1","unstructured":"David Sabbagh Pierre Ablin Ga\u00ebl Varoquaux Alexandre Gramfort and Denis Alexander Engemann. 2019. Manifold-regression to predict from MEG\/EEG brain signals without source modeling. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2834546"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3235040"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475622"},{"key":"e_1_3_2_1_36_1","volume-title":"Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series. In Advances in Neural Information Processing Systems (NeurIPS).","author":"Wang Yihe","year":"2023","unstructured":"Yihe Wang, Yu Han, Haishuai Wang, and Xiang Zhang. 2023. Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612491"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/494"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106007"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2016.2627016"},{"key":"e_1_3_2_1_41_1","unstructured":"Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura and Takahiro Shinozaki. 2021. FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_42_1","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.","author":"Zhang Kexin","year":"2023","unstructured":"Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Y. Liang, Guansong Pang, Dongjin Song, and Shirui Pan. 2023. Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining."},{"key":"e_1_3_2_1_43_1","unstructured":"Xiang Zhang Ziyuan Zhao Theodoros Tsiligkaridis and Marinka Zitnik. 2022. Self-supervised contrastive pre-training for time series via time-frequency consistency. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612000"},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence.","author":"Zhang Yifei","year":"2022","unstructured":"Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, and Irwin King. 2022. Spectral Feature Augmentation for Graph Contrastive Learning and Beyond. In Proceedings of the AAAI Conference on Artificial Intelligence."}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","location":"Melbourne VIC Australia","acronym":"MM '24","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681645","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3681645","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:49Z","timestamp":1750295869000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681645"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":45,"alternative-id":["10.1145\/3664647.3681645","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3681645","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}