{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:06:50Z","timestamp":1778947610716,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":77,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"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\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62322606, 62441605"],"award-info":[{"award-number":["62322606, 62441605"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671953","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"4155-4166","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Brant-X: A Unified Physiological Signal Alignment Framework"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6025-8896","authenticated-orcid":false,"given":"Daoze","family":"Zhang","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5023-1443","authenticated-orcid":false,"given":"Zhizhang","family":"Yuan","sequence":"additional","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\/0009-0007-6990-8833","authenticated-orcid":false,"given":"Kerui","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"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC46164.2021.9629716"},{"key":"e_1_3_2_2_2_1","first-page":"1520","article-title":"Electromyography Monitoring Systems in Rehabilitation: A Review of Clinical Applications","volume":"12","author":"Al-Ayyad Muhammad","year":"2023","unstructured":"Muhammad Al-Ayyad, Hamza Abu Owida, Roberto De Fazio, Bassam Al-Naami, and Paolo Visconti. 2023. Electromyography Monitoring Systems in Rehabilitation: A Review of Clinical Applications, Wearable Devices and Signal Acquisition Methodologies. Electronics, Vol. 12, 7 (2023), 1520.","journal-title":"Wearable Devices and Signal Acquisition Methodologies. Electronics"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105884"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0256111"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03351-1"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/IBCAST54850.2022.9990423"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Richard B Berry Rita Brooks Charlene Gamaldo Susan M Harding Robin M Lloyd Stuart F Quan Matthew T Troester and Bradley V Vaughn. 2017. AASM scoring manual updates for 2017 (version 2.4). 665--666 pages.","DOI":"10.5664\/jcsm.6576"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599426"},{"key":"e_1_3_2_2_9_1","first-page":"12546","article-title":"Contrastive learning of global and local features for medical image segmentation with limited annotations","volume":"33","author":"Chaitanya Krishna","year":"2020","unstructured":"Krishna Chaitanya, Ertunc Erdil, Neerav Karani, and Ender Konukoglu. 2020. Contrastive learning of global and local features for medical image segmentation with limited annotations. Advances in Neural Information Processing Systems, Vol. 33 (2020), 12546--12558.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2016.113"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2972701"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539178"},{"key":"e_1_3_2_2_13_1","volume-title":"AF classification from a short single lead ECG recording: The PhysioNet\/computing in cardiology challenge","author":"Clifford Gari D","year":"2017","unstructured":"Gari D Clifford, Chengyu Liu, Benjamin Moody, H Lehman Li-wei, Ikaro Silva, Qiao Li, AE Johnson, and Roger G Mark. 2017. AF classification from a short single lead ECG recording: The PhysioNet\/computing in cardiology challenge 2017. In Computing in Cardiology (CinC). IEEE, 1--4."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467231"},{"key":"e_1_3_2_2_15_1","unstructured":"Jiaxiang Dong Haixu Wu Haoran Zhang Li Zhang Jianmin Wang and Mingsheng Long. 2023. SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2022.3144169"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/WCONF58270.2023.10235004"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612208"},{"key":"e_1_3_2_2_19_1","volume-title":"The TUH EEG CORPUS: A big data resource for automated EEG interpretation. In 2014 IEEE signal processing in medicine and biology symposium (SPMB)","author":"Harati A","unstructured":"A Harati, S Lopez, I Obeid, J Picone, MP Jacobson, and S Tobochnik. 2014. The TUH EEG CORPUS: A big data resource for automated EEG interpretation. In 2014 IEEE signal processing in medicine and biology symposium (SPMB). IEEE, 1--5."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21155015"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_22_1","volume-title":"Flexible miniaturized sensor technologies for long-term physiological monitoring. npj Flexible Electronics","author":"He Rongyan","year":"2022","unstructured":"Rongyan He, Hao Liu, Yan Niu, Huiqing Zhang, Guy M Genin, and Feng Xu. 2022. Flexible miniaturized sensor technologies for long-term physiological monitoring. npj Flexible Electronics, Vol. 6, 1 (2022), 20."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.21608\/ijicis.2022.99424.1126"},{"key":"e_1_3_2_2_24_1","volume-title":"Niels Birbaumer, and Ujwal Chaudhary.","author":"Jaramillo-Gonzalez Andres","year":"2021","unstructured":"Andres Jaramillo-Gonzalez, Shizhe Wu, Alessandro Tonin, Aygul Rana, Majid Khalili Ardali, Niels Birbaumer, and Ujwal Chaudhary. 2021. A dataset of EEG and EOG from an auditory EOG-based communication system for patients in locked-in state. Scientific data, Vol. 8, 1 (2021), 8."},{"key":"e_1_3_2_2_25_1","volume-title":"ICASSP 2023--2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Jia Ziyu","unstructured":"Ziyu Jia, Youfang Lin, Yuhan Zhou, Xiyang Cai, Peng Zheng, Qiang Li, and Jing Wang. 2023. Exploiting Interactivity and Heterogeneity for Sleep Stage Classification Via Heterogeneous Graph Neural Network. In ICASSP 2023--2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1--5."},{"key":"e_1_3_2_2_26_1","volume-title":"Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI. arXiv preprint arXiv:2405.18765","author":"Jiang Wei-Bang","year":"2024","unstructured":"Wei-Bang Jiang, Li-Ming Zhao, and Bao-Liang Lu. 2024. Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI. arXiv preprint arXiv:2405.18765 (2024)."},{"key":"e_1_3_2_2_27_1","volume-title":"Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728","author":"Jin Ming","year":"2023","unstructured":"Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, et al. 2023. Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728 (2023)."},{"key":"e_1_3_2_2_28_1","volume-title":"A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals. Multimedia Tools and Applications","author":"Kamble Kranti","year":"2023","unstructured":"Kranti Kamble and Joydeep Sengupta. 2023. A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals. Multimedia Tools and Applications (2023), 1--36."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1093\/icb\/icab101"},{"key":"e_1_3_2_2_30_1","volume-title":"DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices","author":"Katsigiannis Stamos","year":"2017","unstructured":"Stamos Katsigiannis and Naeem Ramzan. 2017. DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE journal of biomedical and health informatics, Vol. 22, 1 (2017), 98--107."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/10.867928"},{"key":"e_1_3_2_2_32_1","volume-title":"Jos\u00e9 Moutinho Santos, and Urbano Nunes","author":"Khalighi Sirvan","year":"2016","unstructured":"Sirvan Khalighi, Teresa Sousa, Jos\u00e9 Moutinho Santos, and Urbano Nunes. 2016. ISRUC-Sleep: A comprehensive public dataset for sleep researchers. Computer methods and programs in biomedicine, Vol. 124 (2016), 180--192."},{"key":"e_1_3_2_2_33_1","volume-title":"Thomas H Everett IV, and Bradley S Duerstock","author":"Kirby Ana Karina","year":"2023","unstructured":"Ana Karina Kirby, Sidharth Pancholi, Zada Anderson, Caroline Chesler, Thomas H Everett IV, and Bradley S Duerstock. 2023. Time and frequency domain analysis of physiological features during autonomic dysreflexia after spinal cord injury. Frontiers in Neuroscience, Vol. 17 (2023)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/439"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107380"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103927"},{"key":"e_1_3_2_2_37_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Liu Yuchen","year":"2022","unstructured":"Yuchen Liu and Ziyu Jia. 2022. Bstt: A bayesian spatial-temporal transformer for sleep staging. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00258"},{"key":"e_1_3_2_2_39_1","volume-title":"Visual classification via description from large language models. arXiv preprint arXiv:2210.07183","author":"Menon Sachit","year":"2022","unstructured":"Sachit Menon and Carl Vondrick. 2022. Visual classification via description from large language models. arXiv preprint arXiv:2210.07183 (2022)."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104954"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC48229.2022.9871489"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.06.007"},{"key":"e_1_3_2_2_43_1","volume-title":"International Conference on Learning Representations","author":"Nie Yuqi","year":"2023","unstructured":"Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2023. A time series is worth 64 words: Long-term forecasting with transformers. International Conference on Learning Representations (2023)."},{"key":"e_1_3_2_2_44_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)."},{"key":"e_1_3_2_2_45_1","first-page":"5903","article-title":"XSleepNet: Multi-view sequential model for automatic sleep staging","volume":"44","author":"Phan Huy","year":"2021","unstructured":"Huy Phan, Oliver Y Ch\u00e9n, Minh C Tran, Philipp Koch, Alfred Mertins, and Maarten De Vos. 2021. XSleepNet: Multi-view sequential model for automatic sleep staging. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 9 (2021), 5903--5915.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_2_46_1","volume-title":"L-SeqSleepNet: Whole-cycle long sequence modelling for automatic sleep staging","author":"Phan Huy","year":"2023","unstructured":"Huy Phan, Kristian P Lorenzen, Elisabeth Heremans, Oliver Y Ch\u00e9n, Minh C Tran, Philipp Koch, Alfred Mertins, Mathias Baumert, Kaare B Mikkelsen, and Maarten De Vos. 2023. L-SeqSleepNet: Whole-cycle long sequence modelling for automatic sleep staging. IEEE Journal of Biomedical and Health Informatics (2023)."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ac6049"},{"key":"e_1_3_2_2_48_1","volume-title":"International conference on machine learning.","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20040969"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/JRPROC.1949.232969"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph19127176"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2022.3230250"},{"key":"e_1_3_2_2_53_1","unstructured":"Petre Stoica Randolph L Moses et al. 2005. Spectral analysis of signals. Vol. 452. Pearson Prentice Hall Upper Saddle River NJ."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2022.3199075"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC44109.2020.9176741"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.3025777"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"crossref","unstructured":"Mario Giovanni Terzano Liborio Parrino Adriano Sherieri Ronald Chervin Sudhansu Chokroverty Christian Guilleminault Max Hirshkowitz Mark Mahowald Harvey Moldofsky Agostino Rosa et al. 2001. Atlas rules and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep medicine Vol. 2 6 (2001) 537--554.","DOI":"10.1016\/S1389-9457(01)00149-6"},{"key":"e_1_3_2_2_58_1","volume-title":"Biopotentials and electrophysiology measurement. Measurement, Instrumentation, and Sensors Handbook","author":"Thakor Nitish V","year":"2017","unstructured":"Nitish V Thakor. 2017. Biopotentials and electrophysiology measurement. Measurement, Instrumentation, and Sensors Handbook (2017), 64--1."},{"key":"e_1_3_2_2_59_1","volume-title":"Auditory electrooculogram-based communication system for ALS patients in transition from locked-in to complete locked-in state. Scientific reports","author":"Tonin Alessandro","year":"2020","unstructured":"Alessandro Tonin, Andres Jaramillo-Gonzalez, Aygul Rana, Majid Khalili-Ardali, Niels Birbaumer, and Ujwal Chaudhary. 2020. Auditory electrooculogram-based communication system for ALS patients in transition from locked-in to complete locked-in state. Scientific reports, Vol. 10, 1 (2020), 8452."},{"key":"e_1_3_2_2_60_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Vaswani Ashish","year":"2017","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. Advances in Neural Information Processing Systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_61_1","first-page":"33536","article-title":"Multi-granularity cross-modal alignment for generalized medical visual representation learning","volume":"35","author":"Wang Fuying","year":"2022","unstructured":"Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, and Lequan Yu. 2022. Multi-granularity cross-modal alignment for generalized medical visual representation learning. Advances in Neural Information Processing Systems, Vol. 35 (2022), 33536--33549.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i1.27779"},{"key":"e_1_3_2_2_63_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems","author":"Wang Wenhai","year":"2023","unstructured":"Wenhai Wang, Zhe Chen, Xiaokang Chen, Jiannan Wu, Xizhou Zhu, Gang Zeng, Ping Luo, Tong Lu, Jie Zhou, Yu Qiao, et al. 2023. Visionllm: Large language model is also an open-ended decoder for vision-centric tasks. Thirty-seventh Conference on Neural Information Processing Systems (2023)."},{"key":"e_1_3_2_2_64_1","volume-title":"A Novel Emotion Recognition Method Based on the Feature Fusion of Single-Lead EEG and ECG Signals","author":"Wang Xiaoman","year":"2023","unstructured":"Xiaoman Wang, Jianwen Zhang, Chunhua He, Heng Wu, and Lianglun Cheng. 2023. A Novel Emotion Recognition Method Based on the Feature Fusion of Single-Lead EEG and ECG Signals. IEEE Internet of Things Journal (2023)."},{"key":"e_1_3_2_2_65_1","volume-title":"Timesnet: Temporal 2d-variation modeling for general time series analysis. In The eleventh international conference on learning representations.","author":"Wu Haixu","year":"2022","unstructured":"Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2022. Timesnet: Temporal 2d-variation modeling for general time series analysis. In The eleventh international conference on learning representations."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.3390\/app13084964"},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01857"},{"key":"e_1_3_2_2_68_1","volume-title":"Lijie Grace Zhang, and Huanyu Cheng","author":"Yi Ning","year":"2019","unstructured":"Ning Yi, Haitao Cui, Lijie Grace Zhang, and Huanyu Cheng. 2019. Integration of biological systems with electronic-mechanical assemblies. Acta biomaterialia, Vol. 95 (2019), 91--111."},{"key":"e_1_3_2_2_69_1","volume-title":"Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917","author":"Yu Jiahui","year":"2022","unstructured":"Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. 2022. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917 (2022)."},{"key":"e_1_3_2_2_70_1","volume-title":"Brant-2: Foundation Model for Brain Signals. arXiv preprint","author":"Yuan Zhizhang","year":"2024","unstructured":"Zhizhang Yuan, Daoze Zhang, Junru Chen, Gefei Gu, and Yang Yang. 2024. Brant-2: Foundation Model for Brain Signals. arXiv preprint (2024)."},{"key":"e_1_3_2_2_71_1","volume-title":"PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection. In Thirty-seventh Conference on Neural Information Processing Systems.","author":"Yuan Zhizhang","year":"2023","unstructured":"Zhizhang Yuan, Daoze Zhang, Yang Yang, Junru Chen, and Yafeng Li. 2023. PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_72_1","volume-title":"Trahgr: Transformer for hand gesture recognition via electromyography","author":"Zabihi Soheil","year":"2023","unstructured":"Soheil Zabihi, Elahe Rahimian, Amir Asif, and Arash Mohammadi. 2023. Trahgr: Transformer for hand gesture recognition via electromyography. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023)."},{"key":"e_1_3_2_2_73_1","volume-title":"Brant: Foundation Model for Intracranial Neural Signal. In Thirty-seventh Conference on Neural Information Processing Systems.","author":"Zhang Daoze","year":"2023","unstructured":"Daoze Zhang, Zhizhang Yuan, Yang Yang, Junru Chen, Jingjing Wang, and Yafeng Li. 2023. Brant: Foundation Model for Intracranial Neural Signal. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"crossref","unstructured":"Wei Zhang Zhuokun Yang Hantao Li Debin Huang Lipeng Wang Yanzhao Wei Lei Zhang Lin Ma Huanhuan Feng Jing Pan et al. 2022. Multimodal data for the detection of freezing of gait in Parkinson's disease. Scientific data Vol. 9 1 (2022) 606.","DOI":"10.1038\/s41597-022-01713-8"},{"key":"e_1_3_2_2_75_1","first-page":"3988","article-title":"Self-supervised contrastive pre-training for time series via time-frequency consistency","volume":"35","author":"Zhang Xiang","year":"2022","unstructured":"Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik. 2022. Self-supervised contrastive pre-training for time series via time-frequency consistency. Advances in Neural Information Processing Systems, Vol. 35 (2022), 3988--4003.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_76_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems.","author":"Zhou Tian","year":"2023","unstructured":"Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, and Rong Jin. 2023. One Fits All: Power General Time Series Analysis by Pretrained LM. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCE50685.2021.9427710"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671953","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671953","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:05Z","timestamp":1750291565000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":77,"alternative-id":["10.1145\/3637528.3671953","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671953","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}