{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:20:18Z","timestamp":1780053618437,"version":"3.54.0"},"publisher-location":"New York, NY, USA","reference-count":63,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,13]]},"DOI":"10.1145\/3654777.3676429","type":"proceedings-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T10:50:36Z","timestamp":1728643836000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Empower Real-World BCIs with NIRS-X: An Adaptive Learning Framework that Harnesses Unlabeled Brain Signals"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7253-9070","authenticated-orcid":false,"given":"Liang","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Tufts University, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1668-5181","authenticated-orcid":false,"given":"Jiayan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computer Science, University of San Francisco, United States and Department of Computer Science, Tufts University, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1211-3718","authenticated-orcid":false,"given":"Jinyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Northeastern University, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5522-7999","authenticated-orcid":false,"given":"Devon","family":"McKeon","sequence":"additional","affiliation":[{"name":"Google LLC, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3106-7545","authenticated-orcid":false,"given":"David Guy","family":"Brizan","sequence":"additional","affiliation":[{"name":"Computer Science, University of San Francisco, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3419-4547","authenticated-orcid":false,"given":"Giles","family":"Blaney","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Tufts University, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5983-7388","authenticated-orcid":false,"given":"Robert J.K","family":"Jacob","sequence":"additional","affiliation":[{"name":"Computer Science, Tufts University, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2642918.2647414"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","unstructured":"Arjun Arjun Aniket\u00a0Singh Rajpoot and Mahesh Raveendranatha\u00a0Panicker. 2021. Introducing Attention Mechanism for EEG Signals: Emotion Recognition with Vision Transformers. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 5723\u20135726. https:\/\/doi.org\/10.1109\/EMBC46164.2021.9629837","DOI":"10.1109\/EMBC46164.2021.9629837"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.NPh.9.S2.S24001"},{"key":"e_1_3_2_1_4_1","unstructured":"Shaojie Bai J.\u00a0Zico Kolter and Vladlen Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arxiv:1803.01271\u00a0[cs.LG]"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abca18"},{"key":"e_1_3_2_1_6_1","volume-title":"Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448","author":"Bashivan Pouya","year":"2015","unstructured":"Pouya Bashivan, Irina Rish, Mohammed Yeasin, and Noel Codella. 2015. Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448 (2015)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3363384.3363392"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0015512"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/jbio.201960018"},{"key":"e_1_3_2_1_11_1","volume-title":"Universal sentence encoder. arXiv preprint arXiv:1803.11175","author":"Cer Daniel","year":"2018","unstructured":"Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni\u00a0St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, 2018. Universal sentence encoder. arXiv preprint arXiv:1803.11175 (2018)."},{"key":"e_1_3_2_1_12_1","volume-title":"International conference on machine learning. PMLR, 1597\u20131607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597\u20131607."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"e_1_3_2_1_14_1","unstructured":"Joseph\u00a0Y. Cheng Hanlin Goh Kaan Dogrusoz Oncel Tuzel and Erdrin Azemi. 2020. Subject-Aware Contrastive Learning for Biosignals. arxiv:2007.04871\u00a0[cs.LG]"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aaaf82"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20072034"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2018.00102"},{"key":"e_1_3_2_1_18_1","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly Jakob Uszkoreit and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arxiv:2010.11929\u00a0[cs.CV]"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.NPh.9.4.041411"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3308189"},{"key":"e_1_3_2_1_21_1","volume-title":"A convolutional encoder model for neural machine translation. arXiv preprint arXiv:1611.02344","author":"Gehring Jonas","year":"2016","unstructured":"Jonas Gehring, Michael Auli, David Grangier, and Yann\u00a0N Dauphin. 2016. A convolutional encoder model for neural machine translation. arXiv preprint arXiv:1611.02344 (2016)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/2326263X.2019.1655837"},{"key":"e_1_3_2_1_23_1","volume-title":"Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems 33","author":"Grill Jean-Bastien","year":"2020","unstructured":"Jean-Bastien Grill, Florian Strub, Florent Altch\u00e9, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila\u00a0Pires, Zhaohan Guo, Mohammad Gheshlaghi\u00a0Azar, 2020. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems 33 (2020), 21271\u201321284."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","unstructured":"Sandra\u00a0G. Hart and Lowell\u00a0E. Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In Human Mental Workload Peter\u00a0A. Hancock and Najmedin Meshkati (Eds.). Advances in Psychology Vol.\u00a052. North-Holland 139\u2013183. https:\/\/doi.org\/10.1016\/S0166-4115(08)62386-9","DOI":"10.1016\/S0166-4115(08)62386-9"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/a11050067"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2910342"},{"key":"e_1_3_2_1_28_1","volume-title":"Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).","author":"Huang Zhe","year":"2021","unstructured":"Zhe Huang, Liang Wang, Giles Blaney, Christopher Slaughter, Devon McKeon, Ziyu Zhou, Robert Jacob, and Michael\u00a0C Hughes. 2021. The Tufts fNIRS mental workload dataset & benchmark for brain-computer interfaces that generalize. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.NPh.3.1.010401"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","unstructured":"Yitao Jing Weiqun Wang Jiaxing Wang Yuze Jiao Kexin Xiang Tianyu Lin Weiguo Shi and Zeng-Guang Hou. 2023. Transformer Based Cross-Subject Mental Workload Classification Using FNIRS for Real-World Application. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 1\u20135. https:\/\/doi.org\/10.1109\/EMBC40787.2023.10341167","DOI":"10.1109\/EMBC40787.2023.10341167"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Kosar Khaksari Emma Condy John\u00a0B Millerhagen Afrouz\u00a0A Anderson Hadis Dashtestani and Amir\u00a0H Gandjbakhche. 2019. Effects of Performance and task duration on mental workload during working memory task. In Photonics Vol.\u00a06. MDPI 94.","DOI":"10.3390\/photonics6030094"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1177\/09670335221134206"},{"key":"e_1_3_2_1_33_1","volume-title":"Age differences in short-term retention of rapidly changing information.Journal of experimental psychology 55, 4","author":"Kirchner K","year":"1958","unstructured":"Wayne\u00a0K Kirchner. 1958. Age differences in short-term retention of rapidly changing information.Journal of experimental psychology 55, 4 (1958), 352."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2019.00331"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2021.653659"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abb7a7"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10020186"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107312"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abd2ca"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956291"},{"key":"e_1_3_2_1_42_1","unstructured":"Mostafa\u00a0Neo Mohsenvand Mohammad\u00a0Rasool Izadi and Pattie Maes. 2020. Contrastive representation learning for electroencephalogram classification. In Machine Learning for Health. PMLR 238\u2013253."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2022.873239"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2023.1240660"},{"key":"e_1_3_2_1_45_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van\u00a0den Oord Aaron","year":"2018","unstructured":"Aaron van\u00a0den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1518\/001872008X288349"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1111\/nyas.13948"},{"key":"e_1_3_2_1_48_1","volume-title":"Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, and Tonio Ball.","author":"Schirrmeister Robin\u00a0Tibor","year":"2017","unstructured":"Robin\u00a0Tibor Schirrmeister, Jost\u00a0Tobias Springenberg, Lukas Dominique\u00a0Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, and Tonio Ball. 2017. Deep learning with convolutional neural networks for EEG decoding and visualization. Human brain mapping 38, 11 (2017), 5391\u20135420."},{"key":"e_1_3_2_1_49_1","volume-title":"ACM UIST 2009 Symposium on User Interface Software and Technology.","author":"Solovey Erin\u00a0Treacy","year":"2009","unstructured":"Erin\u00a0Treacy Solovey. 2009. Using your brain for human-computer interaction. In ACM UIST 2009 Symposium on User Interface Software and Technology."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2022.3230250"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/LifeTech52111.2021.9391844"},{"key":"e_1_3_2_1_52_1","volume-title":"What makes for good views for contrastive learning?Advances in neural information processing systems 33","author":"Tian Yonglong","year":"2020","unstructured":"Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. 2020. What makes for good views for contrastive learning?Advances in neural information processing systems 33 (2020), 6827\u20136839."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21186106"},{"key":"e_1_3_2_1_54_1","volume-title":"Visualizing data using t-SNE.Journal of machine learning research 9, 11","author":"Maaten Laurens Van\u00a0der","year":"2008","unstructured":"Laurens Van\u00a0der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE.Journal of machine learning research 9, 11 (2008)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3472749.3474743"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3140531"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106463"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1002\/wcs.1343"},{"key":"e_1_3_2_1_59_1","volume-title":"Self-supervised eeg representation learning for automatic sleep staging. arXiv preprint arXiv:2110.15278","author":"Yang Chaoqi","year":"2021","unstructured":"Chaoqi Yang, Danica Xiao, M\u00a0Brandon Westover, and Jimeng Sun. 2021. Self-supervised eeg representation learning for automatic sleep staging. arXiv preprint arXiv:2110.15278 (2021)."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1080\/14639220210123789"},{"key":"e_1_3_2_1_61_1","volume-title":"How does simsiam avoid collapse without negative samples? a unified understanding with self-supervised contrastive learning. arXiv preprint arXiv:2203.16262","author":"Zhang Chaoning","year":"2022","unstructured":"Chaoning Zhang, Kang Zhang, Chenshuang Zhang, Trung\u00a0X Pham, Chang\u00a0D Yoo, and In\u00a0So Kweon. 2022. How does simsiam avoid collapse without negative samples? a unified understanding with self-supervised contrastive learning. arXiv preprint arXiv:2203.16262 (2022)."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.2967128"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3010780"}],"event":{"name":"UIST '24: The 37th Annual ACM Symposium on User Interface Software and Technology","location":"Pittsburgh PA USA","acronym":"UIST '24"},"container-title":["Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3654777.3676429","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3654777.3676429","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T21:13:30Z","timestamp":1754342010000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3654777.3676429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,11]]},"references-count":63,"alternative-id":["10.1145\/3654777.3676429","10.1145\/3654777"],"URL":"https:\/\/doi.org\/10.1145\/3654777.3676429","relation":{},"subject":[],"published":{"date-parts":[[2024,10,11]]},"assertion":[{"value":"2024-10-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}