{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T20:18:25Z","timestamp":1775852305002,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSFC","award":["62176233"],"award-info":[{"award-number":["62176233"]}]},{"name":"Fundamental Research Funds for the Central Universities"},{"name":"National Key Research and Development Project of China","award":["2018AAA0101900"],"award-info":[{"award-number":["2018AAA0101900"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599426","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:13:58Z","timestamp":1691172838000},"page":"130-141","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5790-7505","authenticated-orcid":false,"given":"Donghong","family":"Cai","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5989-2897","authenticated-orcid":false,"given":"Junru","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5058-4417","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7040-1378","authenticated-orcid":false,"given":"Teng","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9681-939X","authenticated-orcid":false,"given":"Yafeng","family":"Li","sequence":"additional","affiliation":[{"name":"Nuozhu Technology Co., Ltd., Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20092505"},{"key":"e_1_3_2_2_2_1","volume-title":"Aaron Bostrom, James Large, and Eamonn Keogh.","author":"Bagnall Anthony","year":"2017","unstructured":"Anthony Bagnall , Jason Lines , Aaron Bostrom, James Large, and Eamonn Keogh. 2017 . The great time series classification bake off: A review and experimental evaluation of recent algorithmic advances. Data mining and knowledge discovery, Vol. 31 , 3 (2017), 606--660. Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large, and Eamonn Keogh. 2017. The great time series classification bake off: A review and experimental evaluation of recent algorithmic advances. Data mining and knowledge discovery, Vol. 31, 3 (2017), 606--660."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abca18"},{"key":"e_1_3_2_2_4_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel Ziegler Jeffrey Wu Clemens Winter Chris Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language models are few-shot learners. In NeurIPS. 1877--1901.  Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel Ziegler Jeffrey Wu Clemens Winter Chris Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language models are few-shot learners. In NeurIPS. 1877--1901."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Junru Chen Yang Yang Tao Yu Yingying Fan Xiaolong Mo and Carl Yang. 2022. BrainNet: Epileptic wave detection from SEEG with hierarchical graph diffusion learning. In KDD. 2741--2751.  Junru Chen Yang Yang Tao Yu Yingying Fan Xiaolong Mo and Carl Yang. 2022. BrainNet: Epileptic wave detection from SEEG with hierarchical graph diffusion learning. In KDD. 2741--2751.","DOI":"10.1145\/3534678.3539178"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2908285"},{"key":"e_1_3_2_2_7_1","unstructured":"Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In ICML. 1597--1607.  Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In ICML. 1597--1607."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.01.001"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Xinlei Chen and Kaiming He. 2021. Exploring simple siamese representation learning. In CVPR. 15745--15753.  Xinlei Chen and Kaiming He. 2021. Exploring simple siamese representation learning. In CVPR. 15745--15753.","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab0ab5"},{"key":"e_1_3_2_2_11_1","volume-title":"Nature","volume":"587","author":"Davis Zachary W","year":"2020","unstructured":"Zachary W Davis , Lyle Muller , Julio Martinez-Trujillo , Terrence Sejnowski , and John H Reynolds . 2020 . Spontaneous travelling cortical waves gate perception in behaving primates . Nature , Vol. 587 , 7834 (2020), 432--436. Zachary W Davis, Lyle Muller, Julio Martinez-Trujillo, Terrence Sejnowski, and John H Reynolds. 2020. Spontaneous travelling cortical waves gate perception in behaving primates. Nature, Vol. 587, 7834 (2020), 432--436."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467231"},{"key":"e_1_3_2_2_13_1","volume-title":"BERT: Pre-training of deep bidirectional Transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . BERT: Pre-training of deep bidirectional Transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional Transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Carl Doersch Abhinav Gupta and Alexei A Efros. 2015. Unsupervised visual representation learning by context prediction. In ICCV. 1422--1430.  Carl Doersch Abhinav Gupta and Alexei A Efros. 2015. Unsupervised visual representation learning by context prediction. In ICCV. 1422--1430.","DOI":"10.1109\/ICCV.2015.167"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Ruo-Nan Duan Jia-Yi Zhu and Bao-Liang Lu. 2013. Differential entropy feature for EEG-based emotion classification. In NER. 81--84.  Ruo-Nan Duan Jia-Yi Zhu and Bao-Liang Lu. 2013. Differential entropy feature for EEG-based emotion classification. In NER. 81--84.","DOI":"10.1109\/NER.2013.6695876"},{"key":"e_1_3_2_2_16_1","volume-title":"Xiaoli Li, and Cuntai Guan.","author":"Eldele Emadeldeen","year":"2021","unstructured":"Emadeldeen Eldele , Mohamed Ragab , Zhenghua Chen , Min Wu , Chee Keong Kwoh , Xiaoli Li, and Cuntai Guan. 2021 . Time-series representation learning via temporal and contextual contrasting. In IJCAI. 2352--2359. Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan. 2021. Time-series representation learning via temporal and contextual contrasting. In IJCAI. 2352--2359."},{"key":"e_1_3_2_2_17_1","unstructured":"Jean-Yves Franceschi Aymeric Dieuleveut and Martin Jaggi. 2019. Unsupervised scalable representation learning for multivariate time series. In NeurIPS.  Jean-Yves Franceschi Aymeric Dieuleveut and Martin Jaggi. 2019. Unsupervised scalable representation learning for multivariate time series. In NeurIPS."},{"key":"e_1_3_2_2_18_1","volume-title":"Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society","author":"Granger Clive WJ","year":"1969","unstructured":"Clive WJ Granger . 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society ( 1969 ), 424--438. Clive WJ Granger. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society (1969), 424--438."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0242857"},{"key":"e_1_3_2_2_21_1","volume-title":"Adam: A method for stochastic optimization. In ICLR.","author":"Kingma Diederik P","year":"2015","unstructured":"Diederik P Kingma and Jimmy Ba . 2015 . Adam: A method for stochastic optimization. In ICLR. Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Shiba Kuanar Vassilis Athitsos Nityananda Pradhan Arabinda Mishra and K.R. Rao. 2018. Cognitive analysis of working memory load from eeg by a deep recurrent neural network. In ICASSP. 2576--2580.  Shiba Kuanar Vassilis Athitsos Nityananda Pradhan Arabinda Mishra and K.R. Rao. 2018. Cognitive analysis of working memory load from eeg by a deep recurrent neural network. In ICASSP. 2576--2580.","DOI":"10.1109\/ICASSP.2018.8462243"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-019-0040-8"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-86891-y"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Ishan Misra C Lawrence Zitnick and Martial Hebert. 2016. Shuffle and learn: Unsupervised learning using temporal order verification. In ECCV. 527--544.  Ishan Misra C Lawrence Zitnick and Martial Hebert. 2016. Shuffle and learn: Unsupervised learning using temporal order verification. In ECCV. 527--544.","DOI":"10.1007\/978-3-319-46448-0_32"},{"key":"e_1_3_2_2_26_1","volume-title":"Mohammad Rasool Izadi, and Pattie Maes","author":"Mohsenvand Mostafa Neo","year":"2020","unstructured":"Mostafa Neo Mohsenvand , Mohammad Rasool Izadi, and Pattie Maes . 2020 . Contrastive representation learning for Electroencephalogram classification. In PMLR. 238--253. Mostafa Neo Mohsenvand, Mohammad Rasool Izadi, and Pattie Maes. 2020. Contrastive representation learning for Electroencephalogram classification. In PMLR. 238--253."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Saeid Motiian Marco Piccirilli Donald A Adjeroh and Gianfranco Doretto. 2017. Unified deep supervised domain adaptation and generalization. In ICCV. 5715--5725.  Saeid Motiian Marco Piccirilli Donald A Adjeroh and Gianfranco Doretto. 2017. Unified deep supervised domain adaptation and generalization. In ICCV. 5715--5725.","DOI":"10.1109\/ICCV.2017.609"},{"key":"e_1_3_2_2_28_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord , Yazhe Li , and Oriol Vinyals . 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 ( 2018 ). Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"M. Paluszek D. Avirovik Y. Zhou S. Kundu A. Chopra R. Montague and S. Priya. 2015. 11 - Magnetoelectric composites for medical application. In Composite Magnetoelectrics. Woodhead Publishing 297--327.  M. Paluszek D. Avirovik Y. Zhou S. Kundu A. Chopra R. Montague and S. Priya. 2015. 11 - Magnetoelectric composites for medical application. In Composite Magnetoelectrics. Woodhead Publishing 297--327.","DOI":"10.1016\/B978-1-78242-254-9.00011-1"},{"key":"e_1_3_2_2_30_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. In NeurIPS.","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , 2019 . Pytorch: An imperative style, high-performance deep learning library. In NeurIPS. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awt299"},{"key":"e_1_3_2_2_32_1","volume-title":"Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy. Nature communications","author":"Proix Timoth\u00e9e","year":"2018","unstructured":"Timoth\u00e9e Proix , Viktor K Jirsa , Fabrice Bartolomei , Maxime Guye , and Wilson Truccolo . 2018. Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy. Nature communications , Vol. 9 , 1 ( 2018 ), 1--15. Timoth\u00e9e Proix, Viktor K Jirsa, Fabrice Bartolomei, Maxime Guye, and Wilson Truccolo. 2018. Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy. Nature communications, Vol. 9, 1 (2018), 1--15."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.3008792"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0377-7"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2018.00083"},{"key":"e_1_3_2_2_36_1","unstructured":"Chao Shang Jie Chen and Jinbo Bi. 2021. Discrete graph structure learning for forecasting multiple time series. In ICLR.  Chao Shang Jie Chen and Jinbo Bi. 2021. Discrete graph structure learning for forecasting multiple time series. In ICLR."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18115780"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2018.2817622"},{"key":"e_1_3_2_2_39_1","volume-title":"Xuan Zhang, Qianying Huang, Florian Dubost, Daniel Rubin, and Christopher Lee-Messer.","author":"Tang Siyi","year":"2022","unstructured":"Siyi Tang , Jared Dunnmon , Khaled Kamal Saab , Xuan Zhang, Qianying Huang, Florian Dubost, Daniel Rubin, and Christopher Lee-Messer. 2022 . Self-supervised graph neural networks for improved Electroencephalographic seizure analysis. In ICLR. Siyi Tang, Jared Dunnmon, Khaled Kamal Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel Rubin, and Christopher Lee-Messer. 2022. Self-supervised graph neural networks for improved Electroencephalographic seizure analysis. In ICLR."},{"key":"e_1_3_2_2_40_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In NeurIPS.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In NeurIPS."},{"key":"e_1_3_2_2_41_1","volume-title":"Connectivity strength-weighted sparse group representation-based brain network construction for M CI classification. Human brain mapping","author":"Yu Renping","year":"2017","unstructured":"Renping Yu , Han Zhang , Le An , Xiaobo Chen , Zhihui Wei , and Dinggang Shen . 2017. Connectivity strength-weighted sparse group representation-based brain network construction for M CI classification. Human brain mapping , Vol. 38 , 5 ( 2017 ), 2370--2383. Renping Yu, Han Zhang, Le An, Xiaobo Chen, Zhihui Wei, and Dinggang Shen. 2017. Connectivity strength-weighted sparse group representation-based brain network construction for M CI classification. Human brain mapping, Vol. 38, 5 (2017), 2370--2383."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2871678"},{"key":"e_1_3_2_2_43_1","volume-title":"TS2Vec: Towards universal representation of time series. arXiv preprint arXiv:2106.10466","author":"Yue Zhihan","year":"2021","unstructured":"Zhihan Yue , Yujing Wang , Juanyong Duan , Tianmeng Yang , Congrui Huang , Yunhai Tong , and Bixiong Xu. 2021. TS2Vec: Towards universal representation of time series. arXiv preprint arXiv:2106.10466 ( 2021 ). Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu. 2021. TS2Vec: Towards universal representation of time series. arXiv preprint arXiv:2106.10466 (2021)."},{"key":"e_1_3_2_2_44_1","unstructured":"Seongjun Yun Minbyul Jeong Raehyun Kim Jaewoo Kang and Hyunwoo J Kim. 2019. Graph Transformer networks. In NeurIPS.  Seongjun Yun Minbyul Jeong Raehyun Kim Jaewoo Kang and Hyunwoo J Kim. 2019. Graph Transformer networks. In NeurIPS."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","unstructured":"George Zerveas Srideepika Jayaraman Dhaval Patel Anuradha Bhamidipaty and Carsten Eickhoff. 2021. A Transformer-based framework for multivariate time series representation learning. In KDD. 2114--2124.  George Zerveas Srideepika Jayaraman Dhaval Patel Anuradha Bhamidipaty and Carsten Eickhoff. 2021. A Transformer-based framework for multivariate time series representation learning. In KDD. 2114--2124.","DOI":"10.1145\/3447548.3467401"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abc902"},{"key":"e_1_3_2_2_47_1","unstructured":"Xiang Zhang Marko Zeman Theodoros Tsiligkaridis and Marinka Zitnik. 2022. Graph-guided network for irregularly sampled multivariate time series. In ICLR.  Xiang Zhang Marko Zeman Theodoros Tsiligkaridis and Marinka Zitnik. 2022. Graph-guided network for irregularly sampled multivariate time series. In ICLR."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2017.06.013"}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599426","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599426","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:36Z","timestamp":1750178256000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":48,"alternative-id":["10.1145\/3580305.3599426","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599426","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}