{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T17:50:41Z","timestamp":1766598641012,"version":"3.40.3"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031768026"},{"type":"electronic","value":"9783031768033"}],"license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-76803-3_24","type":"book-chapter","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T04:23:15Z","timestamp":1733372595000},"page":"407-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Refining Human-Data Interaction: Advanced Techniques for\u00a0EEGEyeNet Dataset Precision"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2506-9922","authenticated-orcid":false,"given":"Jade","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1138-4074","authenticated-orcid":false,"given":"Jingwen","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3559-1239","authenticated-orcid":false,"given":"Sofia","family":"Utoft","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"issue":"1","key":"24_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3563948","volume":"4","author":"S An","year":"2023","unstructured":"An, S., Bhat, G., Gumussoy, S., Ogras, U.: Transfer learning for human activity recognition using representational analysis of neural networks. ACM Trans. Comput. Healthc. 4(1), 1\u201321 (2023)","journal-title":"ACM Trans. Comput. Healthc."},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"An, S., Tuncel, Y., Basaklar, T., Ogras, U.Y.: A survey of embedded machine learning for smart and sustainable healthcare applications. In: Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Use Cases and Emerging Challenges, pp. 127\u2013150. Springer, Heidelberg (2023b)","DOI":"10.1007\/978-3-031-40677-5_6"},{"key":"24_CR3","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)"},{"issue":"3","key":"24_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3381028","volume":"53","author":"A Boukerche","year":"2020","unstructured":"Boukerche, A., Zheng, L., Alfandi, O.: Outlier detection: methods, models, and classification. ACM Comput. Surv. (CSUR) 53(3), 1\u201337 (2020)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"3","key":"24_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3381028","volume":"53","author":"A Boukerche","year":"2020","unstructured":"Boukerche, A., Zheng, L., Alfandi, O.: Outlier detection: methods, models, and classification. ACM Comput. Surv. (CSUR) 53(3), 1\u201337 (2020). https:\/\/doi.org\/10.1145\/3381028. ISSN 0360-0300","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Chen, P., Ding, H., Araki, J., Huang, R.: Explicitly capturing relations between entity mentions via graph neural networks for domain-specific named entity recognition. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol. 2: Short Papers, pp. 735\u2013742 (2021)","DOI":"10.18653\/v1\/2021.acl-short.93"},{"key":"24_CR7","unstructured":"Chen, P., et al.: Hytrel: hypergraph-enhanced tabular data representation learning. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"24_CR8","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Dou, G., Zhou, Z., Qu, X.: Time majority voting, a pc-based eeg classifier for non-expert users. In: International Conference on Human-Computer Interaction, pp. 415\u2013428. Springer, Heidelberg (2022)","DOI":"10.1007\/978-3-031-17618-0_29"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Farago, E., Law, A.J., Hajra, S.G., Chan, A.D.C.: Blink and saccade detection from forehead eeg. In: 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1\u20136. IEEE (2022)","DOI":"10.1109\/I2MTC48687.2022.9806494"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Fuhl, W., et al.: One step closer to eeg-based eye tracking. In: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, pp. 1\u20137 (2023)","DOI":"10.1145\/3588015.3588423"},{"issue":"2","key":"24_CR12","doi-asserted-by":"publisher","first-page":"327","DOI":"10.3390\/rs16020327","volume":"16","author":"S Gui","year":"2024","unstructured":"Gui, S., Song, S., Qin, R., Tang, Y.: Remote sensing object detection in the deep learning era-a review. Remote Sens. 16(2), 327 (2024)","journal-title":"Remote Sens."},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Ingolfsson, T.M., et\u00a0al.: Eeg-tcnet: an accurate temporal convolutional network for embedded motor-imagery brain-machine interfaces. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE (2020)","DOI":"10.1109\/SMC42975.2020.9283028"},{"key":"24_CR14","unstructured":"Jiang, C., Hui, B., Liu, B., Yan, D.: Successfully applying lottery ticket hypothesis to diffusion model. arXiv preprint arXiv:2310.18823 (2023)"},{"key":"24_CR15","unstructured":"Kastrati, A., Plomecka, M.B., K\u00fcchler, J., Langer, N., Wattenhofer, R.: Electrode clustering and bandpass analysis of eeg data for gaze estimation. In: Annual Conference on Neural Information Processing Systems, pp. 50\u201365. PMLR (2023)"},{"key":"24_CR16","unstructured":"Kastrati, A., et\u00a0al.: Eegeyenet: a simultaneous electroencephalography and eye-tracking dataset and benchmark for eye movement prediction. arXiv preprint arXiv:2111.05100 (2021)"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: Spherehead: stable 3d full-head synthesis with spherical tri-plane representation. arXiv preprint arXiv:2404.05680 (2024)","DOI":"10.1007\/978-3-031-73226-3_19"},{"key":"24_CR18","unstructured":"Lu, Y., Sato, K., Wang, J.: Deep learning based multi-label image classification of protest activities. arXiv preprint arXiv:2301.04212 (2023a)"},{"key":"24_CR19","unstructured":"Lu, Y., Shen, M., Wang, H., Wang, X., van Rechem, C., Wei, W.: Machine learning for synthetic data generation: a review. arXiv preprint arXiv:2302.04062 (2023b)"},{"key":"24_CR20","doi-asserted-by":"publisher","first-page":"0126","DOI":"10.34133\/hds.0126","volume":"4","author":"L Yingzhou","year":"2024","unstructured":"Yingzhou, L., Chen, T., Hao, N., Van Rechem, C., Chen, J., Tianfan, F.: Uncertainty quantification and interpretability for clinical trial approval prediction. Health Data Sci. 4, 0126 (2024)","journal-title":"Health Data Sci."},{"key":"24_CR21","unstructured":"Ma, X.: Traffic performance evaluation using statistical and machine learning methods. PhD thesis, The University of Arizona (2022)"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Ma, X., Karimpour, A., Wu, Y.J.: Data-driven transfer learning framework for estimating on-ramp and off-ramp traffic flows. J. Intell. Transport. Syst. 1\u201314 (2024)","DOI":"10.1080\/15472450.2023.2301696"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Mishra, A.R., et\u00a0al.: Signeeg v1. 0: Multimodal electroencephalography and signature database for biometric systems. bioRxiv, pp. 2023\u201309 (2023)","DOI":"10.1101\/2023.09.09.556960"},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Modesitt, E., Yang, R., Liu, Q.: Two heads are better than one: a bio-inspired method for improving classification on eeg-et data. In: International Conference on Human-Computer Interaction, pp. 382\u2013390. Springer, Heidelberg (2023)","DOI":"10.1007\/978-3-031-35989-7_49"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Modesitt, E., Huang\u00a0Wang, H., Yin, H., Lu, B.: Fusing pretrained vits with tcnet for enhanced eeg regression (2024)","DOI":"10.1007\/978-3-031-61572-6_4"},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Murungi, N.K., Pham, M.V., Dai, X., Qu, X.: Trends in machine learning and electroencephalogram (eeg): a review for undergraduate researchers. In: International Conference on Human-Computer Interaction, pp. 426\u2013443. Springer, Heidelberg (2023a)","DOI":"10.1007\/978-3-031-48038-6_27"},{"key":"24_CR27","unstructured":"Murungi, N.K., Pham, M.V., Dai, X.C., Qu, X.: Empowering computer science students in electroencephalography (eeg) analysis: A review of machine learning algorithms for eeg datasets. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1728\u20131739 (2023b)"},{"key":"24_CR28","doi-asserted-by":"publisher","unstructured":"Nakano, Y.I., Ishii, R.: Estimating user\u2019s engagement from eye-gaze behaviors in human-agent conversations. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, IUI 2010, pp. 139-148. Association for Computing Machinery, New York (2010). ISBN 9781605585154. https:\/\/doi.org\/10.1145\/1719970.1719990","DOI":"10.1145\/1719970.1719990"},{"key":"24_CR29","doi-asserted-by":"publisher","unstructured":"Pedroni, A., Bahreini, A., Langer, N.: Automagic: standardized preprocessing of big eeg data. Neuroimage 200, pp. 460\u2013473 (2019). https:\/\/doi.org\/10.1016\/j.neuroimage.2019.06.046","DOI":"10.1016\/j.neuroimage.2019.06.046"},{"key":"24_CR30","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.neuroimage.2019.05.026","volume":"198","author":"L Pion-Tonachini","year":"2019","unstructured":"Pion-Tonachini, L., Kreutz-Delgado, K., Makeig, S.: Iclabel: an automated electroencephalographic independent component classifier, dataset, and website. Neuroimage 198, 181\u2013197 (2019)","journal-title":"Neuroimage"},{"key":"24_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/978-3-030-60735-7_3","volume-title":"Brain Function Assessment in Learning","author":"X Qu","year":"2020","unstructured":"Qu, X., Liu, P., Li, Z., Hickey, T.: Multi-class time continuity voting for EEG classification. In: Frasson, C., Bamidis, P., Vlamos, P. (eds.) BFAL 2020. LNCS (LNAI), vol. 12462, pp. 24\u201333. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60735-7_3"},{"key":"24_CR32","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/978-3-030-60735-7_7","volume-title":"Brain Function Assessment in Learning","author":"X Qu","year":"2020","unstructured":"Qu, X., Mei, Q., Liu, P., Hickey, T.: Using EEG to distinguish between writing and typing for the same cognitive task. In: Frasson, C., Bamidis, P., Vlamos, P. (eds.) BFAL 2020. LNCS (LNAI), vol. 12462, pp. 66\u201374. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60735-7_7"},{"key":"24_CR33","doi-asserted-by":"crossref","unstructured":"Rolff, T., Harms, H.M., Steinicke, F., Frintrop, S.: Gazetransformer: gaze forecasting for virtual reality using transformer networks. In: DAGM German Conference on Pattern Recognition, pp. 577\u2013593. Springer, Heidelberg (2022)","DOI":"10.1007\/978-3-031-16788-1_35"},{"key":"24_CR34","doi-asserted-by":"crossref","unstructured":"Skoglund, M.A., Andersen, M., Shiell, M.M., Keidser, G., Rank, M.L., Rotger-Griful, S.: Comparing in-ear eog for eye-movement estimation with eye-tracking: accuracy, calibration, and speech comprehension. Front. Neurosci. 16, 873201 (2022)","DOI":"10.3389\/fnins.2022.873201"},{"issue":"5","key":"24_CR35","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ad017d","volume":"20","author":"J Tan","year":"2023","unstructured":"Tan, J., Zhang, X., Shenghui, W., Song, Z., Chen, S., Huang, Y., Wang, Y.: Audio-induced medial prefrontal cortical dynamics enhances coadaptive learning in brain-machine interfaces. J. Neural Eng. 20(5), 056035 (2023)","journal-title":"J. Neural Eng."},{"key":"24_CR36","doi-asserted-by":"crossref","unstructured":"Tan, J., Zhang, X., Wu, S., Wang, Y.: State-space model based inverse reinforcement learning for reward function estimation in brain-machine interfaces. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1\u20134. IEEE (2023b)","DOI":"10.1109\/EMBC40787.2023.10340953"},{"issue":"3","key":"24_CR37","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.3390\/s23031437","volume":"23","author":"Y Tang","year":"2023","unstructured":"Tang, Y., Song, S., Gui, S., Chao, W., Cheng, C., Qin, R.: Active and low-cost hyperspectral imaging for the spectral analysis of a low-light environment. Sensors 23(3), 1437 (2023)","journal-title":"Sensors"},{"issue":"12","key":"24_CR38","doi-asserted-by":"publisher","first-page":"5470","DOI":"10.3390\/s23125470","volume":"23","author":"J Wang","year":"2023","unstructured":"Wang, J., Chang, R., Zhao, Z., Pahwa, R.S.: Robust detection, segmentation, and metrology of high bandwidth memory 3D scans using an improved semi-supervised deep learning approach. Sensors 23(12), 5470 (2023)","journal-title":"Sensors"},{"key":"24_CR39","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, Z.: Cnn with self-attention in eeg classification. In: International Conference on Human-Computer Interaction, pp. 512\u2013526. Springer, Heidelberg (2022)","DOI":"10.1007\/978-3-031-17618-0_36"},{"key":"24_CR40","unstructured":"Wolf, L., et al.: A deep learning approach for the segmentation of electroencephalography data in eye tracking applications. arXiv preprint arXiv:2206.08672 (2022)"},{"key":"24_CR41","doi-asserted-by":"crossref","unstructured":"Xiang, B., Abdelmonsef, A.: Vector-based data improves left-right eye-tracking classifier performance after a covariate distributional shift. In: International Conference on Human-Computer Interaction, pp. 617\u2013632. Springer (2022)","DOI":"10.1007\/978-3-031-17615-9_44"},{"key":"24_CR42","unstructured":"Yang, R., Modesitt, E.: Vit2eeg: leveraging hybrid pretrained vision transformers for eeg data. arXiv preprint arXiv:2308.00454 (2023)"},{"key":"24_CR43","doi-asserted-by":"crossref","unstructured":"Yi, L., Qu, X.: Attention-based cnn capturing eeg recording\u2019s average voltage and local change. In: Artificial Intelligence in HCI: 3rd International Conference, AI-HCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, 26 June\u20131 July 2022, Proceedings, pp. 448\u2013459. Springer, Heidelberg (2022)","DOI":"10.1007\/978-3-031-05643-7_29"},{"key":"24_CR44","doi-asserted-by":"crossref","unstructured":"Yunoki, I., Berreby, G., D\u2019Andrea, N., Lu, Y., Qu, X.: Exploring ai music generation: a review of deep learning algorithms and datasets for undergraduate researchers. In: International Conference on Human-Computer Interaction, pp. 102\u2013116. Springer, Heidelberg (2023)","DOI":"10.1007\/978-3-031-49215-0_13"},{"issue":"2","key":"24_CR45","doi-asserted-by":"publisher","first-page":"1564","DOI":"10.1109\/TIV.2022.3229682","volume":"8","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., Tian, R., Sherony, R., Domeyer, J., Ding, Z.: Attention-based interrelation modeling for explainable automated driving. IEEE Trans. Intell. Veh. 8(2), 1564\u20131573 (2022)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"24_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Tian, R., Ding, Z.: Trep: transformer-based evidential prediction for pedestrian intention with uncertainty. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 3534\u20133542 (2023)","DOI":"10.1609\/aaai.v37i3.25463"},{"key":"24_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, H., Du, H., Yang, S., Yao, F.: Rec-rn: user representations learning over the knowledge graph for recommendation systems. In: 2022 4th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), pp. 228\u2013233. IEEE (2022a)","DOI":"10.1109\/MLBDBI58171.2022.00051"},{"key":"24_CR48","doi-asserted-by":"crossref","unstructured":"Zhao, S., et al.: Deep learning based cetsa feature prediction cross multiple cell lines with latent space representation. Sci. Rep. 14(1), 1878 (2024)","DOI":"10.1038\/s41598-024-51193-6"},{"key":"24_CR49","doi-asserted-by":"crossref","unstructured":"Zhao, Z., et al.: Le-uda: label-efficient unsupervised domain adaptation for medical image segmentation. IEEE Trans. Med. Imaging 42(3), 633\u2013646 (2022b)","DOI":"10.1109\/TMI.2022.3214766"}],"container-title":["Lecture Notes in Computer Science","HCI International 2024 \u2013 Late Breaking Papers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-76803-3_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T10:55:36Z","timestamp":1733396136000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-76803-3_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"ISBN":["9783031768026","9783031768033"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-76803-3_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,6]]},"assertion":[{"value":"6 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}