The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data
We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish...
Saved in:
| Published in | Frontiers in psychology Vol. 13; p. 1028824 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Media S.A
12.01.2023
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard:
www.zuco-benchmark.com
. |
|---|---|
| AbstractList | We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com. We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com.We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com. We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com . |
| Author | Kiegeland, Samuel Özyurt, Yilmazcan Tröndle, Marius Hollenstein, Nora Langer, Nicolas Plomecka, Martyna Jäger, Lena A. |
| AuthorAffiliation | 3 Department of Computer Science, ETH Zurich , Zurich , Switzerland 4 Department of Computational Linguistics, University of Zurich , Zurich , Switzerland 2 Department of Psychology, University of Zurich , Zurich , Switzerland 5 Department of Computer Science, University of Potsdam , Potsdam , Germany 1 Center for Language Technology, University of Copenhagen , Copenhagen , Denmark |
| AuthorAffiliation_xml | – name: 3 Department of Computer Science, ETH Zurich , Zurich , Switzerland – name: 5 Department of Computer Science, University of Potsdam , Potsdam , Germany – name: 1 Center for Language Technology, University of Copenhagen , Copenhagen , Denmark – name: 4 Department of Computational Linguistics, University of Zurich , Zurich , Switzerland – name: 2 Department of Psychology, University of Zurich , Zurich , Switzerland |
| Author_xml | – sequence: 1 givenname: Nora surname: Hollenstein fullname: Hollenstein, Nora – sequence: 2 givenname: Marius surname: Tröndle fullname: Tröndle, Marius – sequence: 3 givenname: Martyna surname: Plomecka fullname: Plomecka, Martyna – sequence: 4 givenname: Samuel surname: Kiegeland fullname: Kiegeland, Samuel – sequence: 5 givenname: Yilmazcan surname: Özyurt fullname: Özyurt, Yilmazcan – sequence: 6 givenname: Lena A. surname: Jäger fullname: Jäger, Lena A. – sequence: 7 givenname: Nicolas surname: Langer fullname: Langer, Nicolas |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36710838$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9Uk1v1DAQtVARLaV_gAPykUsWx0kc54KEVkupVIlLuXDAmtjjXe9m7SV2QPvv8X5QtRzwxR77vTcznveaXPjgkZC3JZtVlew-2F3cL2eccT4rGZeS1y_IVSlEXZSslRdPzpfkJsY1y6tmnDH-ilxWoi2ZrOQV-fGwQvp9mgfao9erLYwbGjzVY4ixiFO_Rp3oiGCcX9IEcUP1ADE66zQkl5G_XVrRxeKWgjcU91ikEfTmgDaQ4A15aWGIeHPer8m3z4uH-Zfi_uvt3fzTfaFrIVNhc2Wil5b30BrbGzBd23TCdq1B0aJFa9pO81qaKjesm0rKTragtTUaRddU1-TupGsCrNVudLmRvQrg1PEijEsFY3J6QFX1mlleGqwZ1KIysi_BMm7rElDmIGt9PGntpn6LOYHPLQ3PRJ-_eLdSy_BL5ZKkkHUWeH8WGMPPCWNSWxc1DgN4DFNUvD38ftMImaHvnuZ6TPJ3QBnAT4DjREa0j5CSqYMR1NEI6mAEdTZCJsl_SNql47hyvW74H_UPqze7cA |
| CitedBy_id | crossref_primary_10_1109_TNSRE_2024_3435460 crossref_primary_10_1080_10447318_2023_2275088 |
| Cites_doi | 10.1016/j.neuroimage.2019.06.046 10.1016/j.jneumeth.2004.03.002 10.1037/xge0000494 10.1101/2020.12.23.424091 10.1016/j.array.2021.100087 10.1088/1741-2552/abc742 10.1080/23273798.2018.1546882 10.1145/2168556.2168575 10.1037/a0023885 10.1016/j.cub.2018.01.080 10.1038/s41467-018-03068-4 10.48550/arXiv.1902.03570 10.1016/j.neuroimage.2020.116934 10.18653/v1/K18-1030 10.3758/s13428-016-0734-0 10.48550/arXiv.1904.02682 10.1016/j.neuroimage.2019.02.057 10.1016/j.neuroimage.2019.05.026 10.1016/j.neuroimage.2019.116356 10.48550/arXiv.2104.11178 10.24963/ijcai.2020/683 10.1145/3314111.3319916 10.1002/hbm.23814 10.1371/journal.pone.0207741 10.1080/23273798.2019.1584679 10.1016/j.cortex.2021.07.005 10.31234/osf.io/j5fgd 10.1016/j.neuroimage.2020.117254 10.1007/s10548-018-0629-8 10.17605/OSF.IO/DN5HP 10.1017/S0952523810000283 10.1016/j.intcom.2011.04.007 10.48550/arXiv.2112.02690 10.1016/j.tics.2003.09.010 10.18653/v1/2021.cmcl-1.26 10.1080/00461520.2015.1004068 10.1145/2950112.2964585 10.1111/lnc3.12198 10.3758/s13428-011-0146-0 10.1016/j.neuroimage.2017.11.062 10.18653/v1/2021.acl-short.19 10.1016/j.cmpb.2021.105941 10.1371/journal.pone.0146848 10.1080/23273798.2018.1499946 10.1016/j.dib.2018.11.044 10.1016/j.jneumeth.2007.08.010 10.7717/peerj.7838 10.3389/fnhum.2014.01024 10.1007/s00426-011-0376-x 10.3758/s13428-017-0908-4 10.1016/j.cortex.2015.04.011 10.18653/v1/2021.cmcl-1.10 10.1093/scan/nsz037 10.1007/978-3-030-63007-2_61 10.1016/j.neuropsychologia.2019.107307 10.1037/h0057532 10.1111/cogs.12461 10.18653/v1/2021.cmcl-1.7 10.1038/sdata.2018.291 10.1017/CBO9781107323667.011 10.1163/156856897X00357 10.3389/fnhum.2021.659410 |
| ContentType | Journal Article |
| Copyright | Copyright © 2023 Hollenstein, Tröndle, Plomecka, Kiegeland, Özyurt, Jäger and Langer. Copyright © 2023 Hollenstein, Tröndle, Plomecka, Kiegeland, Özyurt, Jäger and Langer. 2023 Hollenstein, Tröndle, Plomecka, Kiegeland, Özyurt, Jäger and Langer |
| Copyright_xml | – notice: Copyright © 2023 Hollenstein, Tröndle, Plomecka, Kiegeland, Özyurt, Jäger and Langer. – notice: Copyright © 2023 Hollenstein, Tröndle, Plomecka, Kiegeland, Özyurt, Jäger and Langer. 2023 Hollenstein, Tröndle, Plomecka, Kiegeland, Özyurt, Jäger and Langer |
| DBID | AAYXX CITATION NPM 7X8 5PM DOA |
| DOI | 10.3389/fpsyg.2022.1028824 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Psychology |
| EISSN | 1664-1078 |
| ExternalDocumentID | oai_doaj_org_article_3bc0f21de40a463d8b1af02f41ae88b1 PMC9878684 36710838 10_3389_fpsyg_2022_1028824 |
| Genre | Journal Article |
| GroupedDBID | 53G 5VS 9T4 AAFWJ AAKDD AAYXX ABIVO ACGFO ACGFS ACHQT ACXDI ADBBV ADRAZ AEGXH AFPKN AIAGR ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BCNDV CITATION DIK EBS EJD EMOBN F5P GROUPED_DOAJ GX1 HYE KQ8 M48 M~E O5R O5S OK1 P2P PGMZT RNS RPM IPNFZ NPM RIG 7X8 5PM |
| ID | FETCH-LOGICAL-c468t-f4026b8f2ba7dfbdad97596f97de67efefd79c248d3022c5388987accfdce6953 |
| IEDL.DBID | M48 |
| ISSN | 1664-1078 |
| IngestDate | Wed Aug 27 01:25:50 EDT 2025 Thu Aug 21 18:38:37 EDT 2025 Fri Jul 11 09:46:54 EDT 2025 Mon Jul 21 05:42:36 EDT 2025 Thu Apr 24 22:59:13 EDT 2025 Tue Jul 01 01:35:43 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | reading research reading task classification eye-tracking machine learning EEG cross-subject evaluation |
| Language | English |
| License | Copyright © 2023 Hollenstein, Tröndle, Plomecka, Kiegeland, Özyurt, Jäger and Langer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c468t-f4026b8f2ba7dfbdad97596f97de67efefd79c248d3022c5388987accfdce6953 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Xiaowei Zhao, Emmanuel College, United States Reviewed by: Michael Wolmetz, Johns Hopkins University, United States; Christoph Aurnhammer, Saarland University, Germany; Nicolas Dirix, Ghent University, Belgium This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.3389/fpsyg.2022.1028824 |
| PMID | 36710838 |
| PQID | 2771085568 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_3bc0f21de40a463d8b1af02f41ae88b1 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9878684 proquest_miscellaneous_2771085568 pubmed_primary_36710838 crossref_primary_10_3389_fpsyg_2022_1028824 crossref_citationtrail_10_3389_fpsyg_2022_1028824 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2023-01-12 |
| PublicationDateYYYYMMDD | 2023-01-12 |
| PublicationDate_xml | – month: 01 year: 2023 text: 2023-01-12 day: 12 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland |
| PublicationTitle | Frontiers in psychology |
| PublicationTitleAlternate | Front Psychol |
| PublicationYear | 2023 |
| Publisher | Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media S.A |
| References | Ceh (B15) 2021; 143 B21 Hollenstein (B36) Kelton (B45) 2019 Alday (B3) 2019; 34 Ehinger (B28) 2019; 7 McGuire (B54) 2021 Brennan (B11) 2019; 14 Hollenstein (B40) Rämä (B66) 2010; 27 Pfeiffer (B63) 2020; 218 Abdelrahman (B1) 2019 Bruns (B14) 2004; 137 Kandylaki (B44) 2019; 34 Lobo (B48) 2016 Raatikainen (B65) 2021; 12 Degno (B24) 2019; 148 Frank (B32) 2013 Hollenstein (B37) 2021 Luke (B50) 2018; 50 de Cheveignè (B23) 2020; 207 Michel (B55) 2018; 180 Pereira (B62) 2018 Cop (B19) 2017; 49 Hollenstein (B39) Akbari (B2) 2021 Notaro (B60) 2018; 21 Jäger (B43) 2020 Pedroni (B61) 2019; 200 [Dataset] Jäger (B22) 2021 Chiang (B16) 2019 Lemhöfer (B47) 2012; 44 Lopopolo (B49) 2018 Nastase (B59) 2021 Sereno (B70) 2003; 7 Finke (B29) 2016; 11 Mathur (B52) 2021 Wang (B76) 2021 Barrett (B4) 2018 Frey (B33) 2018; 31 Sun (B72) 2020 Hollenstein (B38) Brouwer (B13) 2017; 41 Schalk (B68) 2008; 167 Tomanek (B74) 2010 Mathias (B51) 2020 Dehghani (B25) 2017; 38 Hollenstein (B42) 2020 Tokunaga (B73) 2017 Bautista (B5) 2020 Brennan (B10) 2016; 10 Hasson (B35) 2015 Bestgen (B6) 2021 MathWorks (B53) 2000 Hamilton (B34) 2020; 35 Tor (B75) 2021 Frank (B31) 2022 Choi (B17) 2014; 8 Kliegl (B46) 2012; 76 Rello (B67) 2015 Broderick (B12) 2018; 28 Biedert (B7) 2012 Cole (B18) 2011; 23 Bonhage (B8) 2015; 68 Dimigen (B27) 2011 Yadav (B77) 2019 Scheinost (B69) 2019; 193 Flesch (B30) 1948; 32 Pion-Tonachini (B64) 2019; 198 Nastase (B58) 2020; 222 Miller (B56) 2015; 50 Culotta (B20) 2006 Nastase (B57) 2019; 14 Brainard (B9) 1997; 10 Devlin (B26) 2019 Hollenstein (B41) 2018; 5 Shain (B71) 2020; 138 |
| References_xml | – volume: 200 start-page: 460 year: 2019 ident: B61 article-title: Automagic: standardized preprocessing of big EEG data publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.06.046 – volume: 137 start-page: 321 year: 2004 ident: B14 article-title: Fourier-, hilbert-and wavelet-based signal analysis: are they really different approaches? publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2004.03.002 – start-page: 1 volume-title: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies year: 2019 ident: B1 article-title: “Classifying attention types with thermal imaging and eye tracking,” – volume: 148 start-page: 453 year: 2019 ident: B24 article-title: Parafoveal previews and lexical frequency in natural reading: evidence from eye movements and fixation-related potentials publication-title: J. Exp. Psychol. Gen doi: 10.1037/xge0000494 – year: 2021 ident: B59 article-title: Narratives: fMRI data for evaluating models of naturalistic language comprehension publication-title: bioRxiv doi: 10.1101/2020.12.23.424091 – volume: 12 start-page: 100087 year: 2021 ident: B65 article-title: Detection of developmental dyslexia with machine learning using eye movement data publication-title: Array doi: 10.1016/j.array.2021.100087 – year: 2020 ident: B72 article-title: Brain2Char: a deep architecture for decoding text from brain recordings publication-title: J. Neural Eng doi: 10.1088/1741-2552/abc742 – volume: 34 start-page: 457 year: 2019 ident: B3 article-title: M/EEG analysis of naturalistic stories: a review from speech to language processing publication-title: Lang. Cogn. Neurosci doi: 10.1080/23273798.2018.1546882 – start-page: 123 volume-title: Proceedings of the Symposium on Eye Tracking Research and Applications year: 2012 ident: B7 article-title: “A robust realtime reading-skimming classifier,” doi: 10.1145/2168556.2168575 – year: 2011 ident: B27 article-title: Coregistration of eye movements and EEG in natural reading: analyses and review publication-title: J. Exp. Psychol. Gen doi: 10.1037/a0023885 – volume: 28 start-page: 803 year: 2018 ident: B12 article-title: Electrophysiological correlates of semantic dissimilarity reflect the comprehension of natural, narrative speech publication-title: Curr. Biol doi: 10.1016/j.cub.2018.01.080 – start-page: 138 year: 2020 ident: B42 article-title: “ZuCo 2.0: a dataset of physiological recordings during natural reading and annotation,” publication-title: Proceedings of The 12th Language Resources and Evaluation Conference – year: 2018 ident: B62 article-title: Toward a universal decoder of linguistic meaning from brain activation publication-title: Nat. Commun doi: 10.1038/s41467-018-03068-4 – year: 2019 ident: B77 article-title: EvalAI: towards better evaluation systems for AI agents publication-title: arXiv doi: 10.48550/arXiv.1902.03570 – volume: 218 start-page: 116934 year: 2020 ident: B63 article-title: Neural dynamics of sentiment processing during naturalistic sentence reading publication-title: Neuroimage doi: 10.1016/j.neuroimage.2020.116934 – start-page: 302 volume-title: Proceedings of the 22nd Conference on Computational Natural Language Learning year: 2018 ident: B4 article-title: “Sequence classification with human attention,” doi: 10.18653/v1/K18-1030 – volume: 49 start-page: 602 year: 2017 ident: B19 article-title: Presenting GECO: an eyetracking corpus of monolingual and bilingual sentence reading publication-title: Behav. Res. Methods doi: 10.3758/s13428-016-0734-0 – start-page: 538 volume-title: Proceedings of the 23nd Conference on Computational Natural Language Learning ident: B39 article-title: “CogniVal: a framework for cognitive word embedding evaluation,” – ident: B21 – ident: B36 article-title: Advancing NLP with cognitive language processing signals publication-title: arXiv [Preprint] doi: 10.48550/arXiv.1904.02682 – volume: 193 start-page: 35 year: 2019 ident: B69 article-title: Ten simple rules for predictive modeling of individual differences in neuroimaging publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.02.057 – volume: 198 start-page: 181 year: 2019 ident: B64 article-title: Iclabel: an automated electroencephalographic independent component classifier, dataset, and website publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.05.026 – volume: 207 start-page: 116356 year: 2020 ident: B23 article-title: Zapline: a simple and effective method to remove power line artifacts publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.116356 – year: 2021 ident: B2 article-title: VATT: transformers for multimodal self-supervised learning from raw video, audio and text publication-title: arXiv doi: 10.48550/arXiv.2104.11178 – start-page: 8 volume-title: LREC 2018 Workshop on Linguistic and Neuro-Cognitive Resources (LiNCR) year: 2018 ident: B49 article-title: “The Narrative Brain Dataset (NBD), an fMRI dataset for the study of natural language processing in the brain,” – start-page: 296 volume-title: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics year: 2006 ident: B20 article-title: “Integrating probabilistic extraction models and data mining to discover relations and patterns in text,” – start-page: 4907 year: 2020 ident: B51 article-title: “A survey on using gaze behaviour for natural language processing,” publication-title: Proceedings of the 29th International Joint Conference on Artificial Intelligence doi: 10.24963/ijcai.2020/683 – start-page: 1 volume-title: Proceedings of the 11th ACM Symposium on Eye Tracking Research and Applications year: 2019 ident: B45 article-title: “Reading detection in real-time,” doi: 10.1145/3314111.3319916 – volume: 38 start-page: 6096 year: 2017 ident: B25 article-title: Decoding the neural representation of story meanings across languages publication-title: Hum. Brain Mapp doi: 10.1002/hbm.23814 – volume: 14 start-page: e0207741 year: 2019 ident: B11 article-title: Hierarchical structure guides rapid linguistic predictions during naturalistic listening publication-title: PLoS ONE doi: 10.1371/journal.pone.0207741 – volume: 34 start-page: 405 year: 2019 ident: B44 article-title: From story comprehension to the neurobiology of language publication-title: Lang. Cogn. Neurosci doi: 10.1080/23273798.2019.1584679 – volume: 143 start-page: 29 year: 2021 ident: B15 article-title: Neurophysiological indicators of internal attention: an fMRI-eye-tracking coregistration study publication-title: Cortex doi: 10.1016/j.cortex.2021.07.005 – start-page: 4171 volume-title: 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies year: 2019 ident: B26 article-title: “BERT: pre-training of deep bidirectional transformers for language understanding,” – year: 2022 ident: B31 article-title: An eye-tracking-with-EEG coregistration corpus of narrative sentences publication-title: PsyArXiv doi: 10.31234/osf.io/j5fgd – start-page: 1 volume-title: Proceedings of the 12th International Web for All Conference year: 2015 ident: B67 article-title: “Detecting readers with dyslexia using machine learning with eye tracking measures,” – start-page: 299 volume-title: in Machine Learning and Knowledge Discovery in Databases. Proceedings of the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases year: 2020 ident: B43 article-title: “Deep eyedentification: biometric identification using micro-movements of the eye,ℍ – volume: 222 start-page: 117254 year: 2020 ident: B58 article-title: Keep it real: rethinking the primacy of experimental control in cognitive neuroscience publication-title: Neuroimage doi: 10.1016/j.neuroimage.2020.117254 – volume: 31 start-page: 640 year: 2018 ident: B33 article-title: An eye fixation-related potential study in two reading tasks: reading to memorize and reading to make a decision publication-title: Brain Topogr doi: 10.1007/s10548-018-0629-8 – year: 2021 ident: B22 article-title: Potsdam Textbook Corpus (PoTeC): Eye Tracking Data from Experts and Non-experts Reading Scientific Texts doi: 10.17605/OSF.IO/DN5HP – start-page: 1250 volume-title: IEEE International Conference on Acoustics, Speech and Signal Processing year: 2021 ident: B52 article-title: “Dynamic graph modeling of simultaneous EEG and eye-tracking data for reading task identification,” – volume: 27 start-page: 187 year: 2010 ident: B66 article-title: Eye fixation-related potentials (EFRPs) during object identification publication-title: Vis. Neurosci doi: 10.1017/S0952523810000283 – volume: 23 start-page: 346 year: 2011 ident: B18 article-title: Task and user effects on reading patterns in information search publication-title: Interact. Comput doi: 10.1016/j.intcom.2011.04.007 – year: 2021 ident: B76 article-title: Open vocabulary electroencephalography-to-text decoding and zero-shot sentiment classification publication-title: arXiv doi: 10.48550/arXiv.2112.02690 – volume: 7 start-page: 489 year: 2003 ident: B70 article-title: Measuring word recognition in reading: eye movements and event-related potentials publication-title: Trends Cogn. Sci doi: 10.1016/j.tics.2003.09.010 – start-page: 222 year: 2021 ident: B54 article-title: “Relation classification with cognitive attention supervision,” publication-title: Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics doi: 10.18653/v1/2021.cmcl-1.26 – start-page: 1158 year: 2010 ident: B74 article-title: “A cognitive cost model of annotations based on eye-tracking data,” publication-title: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics – volume: 50 start-page: 31 year: 2015 ident: B56 article-title: Using reading times and eye-movements to measure cognitive engagement publication-title: Educ. Psychol doi: 10.1080/00461520.2015.1004068 – start-page: 1 volume-title: Proceedings of the International Conference on Human-Computer Interaction in Aerospace year: 2016 ident: B48 article-title: “Cognitive workload classification using eye-tracking and EEG data,” doi: 10.1145/2950112.2964585 – volume: 10 start-page: 299 year: 2016 ident: B10 article-title: Naturalistic sentence comprehension in the brain publication-title: Lang. Linguist. Compass doi: 10.1111/lnc3.12198 – volume: 44 start-page: 325 year: 2012 ident: B47 article-title: Introducing LexTALE: a quick and valid lexical test for advanced learners of English publication-title: Behav. Res. Methods doi: 10.3758/s13428-011-0146-0 – volume: 180 start-page: 577 year: 2018 ident: B55 article-title: EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review publication-title: Neuroimage doi: 10.1016/j.neuroimage.2017.11.062 – start-page: 758 volume-title: Proceedings of the International Conference Recent Advances in Natural Language Processing year: 2017 ident: B73 article-title: An eye-tracking study of named entity annotation – start-page: 141 year: 2021 ident: B37 article-title: “Relative importance in sentence processing,” doi: 10.18653/v1/2021.acl-short.19 – year: 2021 ident: B75 article-title: Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals publication-title: Comput. Methods Programs Biomed doi: 10.1016/j.cmpb.2021.105941 – volume: 11 start-page: e0146848 year: 2016 ident: B29 article-title: Toward FRP-based brain-machine interfaces–single–trial classification of fixation-related potentials publication-title: PLoS ONE doi: 10.1371/journal.pone.0146848 – volume: 35 start-page: 573 year: 2020 ident: B34 article-title: The revolution will not be controlled: natural stimuli in speech neuroscience publication-title: Lang. Cogn. Neurosci doi: 10.1080/23273798.2018.1499946 – volume: 21 start-page: 1937 year: 2018 ident: B60 article-title: Simultaneous EEG, eye-tracking, behavioral, and screen-capture data during online German language learning publication-title: Data Brief doi: 10.1016/j.dib.2018.11.044 – start-page: 878 volume-title: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics year: 2013 ident: B32 article-title: “Word surprisal predicts n400 amplitude during reading,” – volume: 167 start-page: 51 year: 2008 ident: B68 article-title: Brain-computer interfaces (BCIS): detection instead of classification publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2007.08.010 – volume: 7 start-page: e7838 year: 2019 ident: B28 article-title: Unfold: an integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis publication-title: PeerJ doi: 10.7717/peerj.7838 – volume: 8 start-page: 1024 year: 2014 ident: B17 article-title: The neural substrates of natural reading: a comparison of normal and nonword text using eyetracking and fmri publication-title: Front. Hum. Neurosci doi: 10.3389/fnhum.2014.01024 – volume: 76 start-page: 145 year: 2012 ident: B46 article-title: Eye movements and brain electric potentials during reading publication-title: Psychol. Res doi: 10.1007/s00426-011-0376-x – volume: 50 start-page: 826 year: 2018 ident: B50 article-title: The provo corpus: a large eye-tracking corpus with predictability norms publication-title: Behav. Res. Methods doi: 10.3758/s13428-017-0908-4 – volume: 68 start-page: 33 year: 2015 ident: B8 article-title: Combined eye tracking and fMRI reveals neural basis of linguistic predictions during sentence comprehension publication-title: Cortex doi: 10.1016/j.cortex.2015.04.011 – start-page: 90 year: 2021 ident: B6 article-title: “LAST at CMCL 2021 shared task: predicting gaze data during reading with a gradient boosting decision tree approach,” publication-title: Proceedings of the NAACL Workshop on Cognitive Modeling and Computational Linguistics doi: 10.18653/v1/2021.cmcl-1.10 – volume: 14 start-page: 667 year: 2019 ident: B57 article-title: Measuring shared responses across subjects using intersubject correlation publication-title: Soc. Cogn. Affect. Neurosci doi: 10.1093/scan/nsz037 – start-page: 779 volume-title: International Conference on Computational Collective Intelligence year: 2020 ident: B5 article-title: “Towards learning to read like humans,” doi: 10.1007/978-3-030-63007-2_61 – volume: 138 start-page: 107307 year: 2020 ident: B71 article-title: fMRI reveals language-specific predictive coding during naturalistic sentence comprehension publication-title: Neuropsychologia doi: 10.1016/j.neuropsychologia.2019.107307 – volume: 32 start-page: 221 year: 1948 ident: B30 article-title: A new readability yardstick publication-title: J. Appl. Psychol doi: 10.1037/h0057532 – volume: 41 start-page: 1318 year: 2017 ident: B13 article-title: A neurocomputational model of the N400 and the P600 in language processing publication-title: Cogn. Sci doi: 10.1111/cogs.12461 – start-page: 72 ident: B38 article-title: “CMCL 2021 shared task on eye-tracking prediction,” publication-title: Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics doi: 10.18653/v1/2021.cmcl-1.7 – volume: 5 start-page: 180291 year: 2018 ident: B41 article-title: ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading publication-title: Sci. Data doi: 10.1038/sdata.2018.291 – year: 2000 ident: B53 publication-title: MATLAB: The Language of Technical Computing. External interfaces – start-page: 228 volume-title: Cognitive Neuroscience of Natural Language Use year: 2015 ident: B35 article-title: “What are naturalistic comprehension paradigms teaching us about language?” doi: 10.1017/CBO9781107323667.011 – volume: 10 start-page: 433 year: 1997 ident: B9 article-title: The psychophysics toolbox publication-title: Spat. Vis doi: 10.1163/156856897X00357 – start-page: 424 volume-title: 9th International IEEE/EMBS Conference on Neural Engineering year: 2019 ident: B16 article-title: “Cross-subject transfer learning improves the practicality of real-world applications of brain-computer interfaces,” – ident: B40 article-title: Decoding EEG brain activity for multi-modal natural language processing publication-title: Front. Hum. Neurosci doi: 10.3389/fnhum.2021.659410 |
| SSID | ssj0000402002 |
| Score | 2.3815255 |
| Snippet | We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 1028824 |
| SubjectTerms | cross-subject evaluation EEG eye-tracking machine learning Psychology reading research reading task classification |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT-QwDI4QJy6I5-7wUpC4oYo2TfM4AhpASHACCXEgynNZsXTQPA7z77HbMppBaLlwbJOoyWfHsV3HJuRISes1kyGzTvqMO9hzOk-gyIkichu9yiu84HxzK67u-fVD9TBX6gtjwtr0wC1wJ6XzMLYIkeeWizIoV9iUs8QLGxU8oPSFM2_OmGpkMJpFGLqDt2TACtMn6W00_QP2IGOYrQD0Sr5wEjUJ-7_SMj8HS86dPhdrZLVTG-lpO911shTrDbIyk17TTfIEBKePk_MBdcB4z692-EIHNW2-nY0mDv0tdNiGzNOxHb1Qj4ozRgo1xKHokaX9_iW1daBxGjOYh0dHOsUo0i1yf9G_O7_KuuIJmedCjbMECAinEnNWhuSCDVpWWiQtQxQyppiC1J5xFUrAxIPcUxoJ5xOsVOiq3CbL9aCOvwlNlVXeicqXQnI4U7X22hV5KpNgIa_yHik-gDS-yyyOBS7-GbAwEHzTgG8QfNOB3yPHszFvbV6N__Y-Q_rMemJO7OYFcIrpOMV8xyk9cvhBXQN7CH-M2DoOJiPDpMRLGJVQPfKrpfbsU7BmaCuhRS7wwcJcFlvqv89Nnm7AUwnFd35i8rtkBQvdo_OnYHtkeTycxH1Qh8buoOH8d5PMC5o priority: 102 providerName: Directory of Open Access Journals |
| Title | The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36710838 https://www.proquest.com/docview/2771085568 https://pubmed.ncbi.nlm.nih.gov/PMC9878684 https://doaj.org/article/3bc0f21de40a463d8b1af02f41ae88b1 |
| Volume | 13 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELZKufSCeLMFKiNxQ4HEcfw4IATVlgqpnFip4oDlZ1u1JCXZldh_z4w3u2JR4cAxsS3H34w9j4xnCHmppPWayVBYJ33BHew5XSZQ5EQVuY1elQ1ecD75LI5n_NNpc7pD1uWORgCHG007rCc1669e__yxfAcb_i1anCBv36TrYXkGph5jmIgAVEZ-i9wGySSxosHJqO7nkxmNJQzo2f_70C35lNP436R7_hlC-ZtMOrpL7ozKJH2_ov49shPb-2Rvc6YtH5BvwAb06-Kwow7Y8fy77S9p19I8dzEsHHphaL8KpKdzO1xSj-o0xg9lklH009Lp9CO1baBxGQv4Do_udYqxpQ_J7Gj65fC4GEsqFJ4LNS8SICCcSsxZGZILNmjZaJG0DFHImGIKUnvGVagBEw-nodJITp9gpUI39SOy23ZtfEJoaqzyTjS-FpKDpNXaa1eVqU6ChbIpJ6RaA2n8mG8cy15cGbA7EHyTwTcIvhnBn5BXmzHXq2wb_-z9Aemz6YmZsvOLrj8z48YztfPAe1WIvLRc1EG5yqaSJV7ZqOBhQl6sqWtgZ-HvEtvGbjEYJiVezWiEmpDHK2pvpoI1Q1sNLXKLD7a-ZbulvTjP2bsBTyUU3__vkU_JHta8Rz9QxZ6R3Xm_iM9BM5q7g-xROMhM_wtjeBM8 |
| linkProvider | Scholars Portal |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+ZuCo+benchmark+on+cross-subject+reading+task+classification+with+EEG+and+eye-tracking+data&rft.jtitle=Frontiers+in+psychology&rft.au=Hollenstein%2C+Nora&rft.au=Tr%C3%B6ndle%2C+Marius&rft.au=Plomecka%2C+Martyna&rft.au=Kiegeland%2C+Samuel&rft.date=2023-01-12&rft.pub=Frontiers+Media+S.A&rft.eissn=1664-1078&rft.volume=13&rft_id=info:doi/10.3389%2Ffpsyg.2022.1028824&rft.externalDocID=PMC9878684 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1664-1078&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1664-1078&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1664-1078&client=summon |