Integrating Active Brain-Computer Interfaces (aBCIs) with Passive BCIs (pBCIs) under Different Frustration Levels

Integrating Active Brain-Computer Interfaces (aBCIs) with Passive BCIs (pBCIs) under Different Frustration Levels | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrating Active Brain-Computer Interfaces (aBCIs) with Passive BCIs (pBCIs) under Different Frustration Levels Xin Gao, Haipeng Lin, Xiaolong Wu, Dingguo Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6906475/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract The mental state of the users can significantly affect the performance of active brain-computer interfaces (aBCIs). In this work, we aim to adopt passive BCIs (pBCIs) to measure a typical mental state, frustration, that is much relevant to aBCIs. A novel paradigm has been developed that combines both aBCIs and pBCIs under different frustration levels of users. The aBCI in this work is based on classic binary motor imagery (MI). In experiments, a new strategy was implemented that uses visual feedback to induce different levels of frustration. The electroencephalography (EEG) data collected were used for both aBCIs and pBCIs. The pBCI was utilized to assess the frustration level during the aBCI tasks, and the aBCI classification models for different levels of frustration were trained. For pBCI, the filter bank common spatial pattern (FBCSP) feature extraction and support vector machine (SVM) classification were utilized to classify three (i.e., low, moderate, high) frustration levels. For aBCI, the same method (FBCSP+SVM) was used to classify left versus right MI. We also aim to improve the performance of aBCIs in such conditions, so we developed two new methods to incorporate the pBCI results to adapt three MI classifiers to the varying states of frustration. Compared to the conventional approach of directly classifying MI tasks without considering frustration, the two proposed methods has increased the mean classification accuracy by 7.52% and 8.62%, respectively. Starting from "experience perception", this paper rises to the engineering level of "real-time state recognition and model dynamic adaptation", and makes a relatively obvious contribution to the depth of EEG data analysis and methodological integrity. Physical sciences/Engineering Physical sciences/Engineering/Biomedical engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers invited by journal 31 Jul, 2025 Editor assigned by journal 24 Jun, 2025 Submission checks completed at journal 20 Jun, 2025 First submitted to journal 20 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6906475","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":494482307,"identity":"30934062-2907-4b27-be5a-f72510bb199b","order_by":0,"name":"Xin Gao","email":"","orcid":"","institution":"University of Bath","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Gao","suffix":""},{"id":494482308,"identity":"891fb1b1-069b-4080-8f91-06cbc7460ebb","order_by":1,"name":"Haipeng Lin","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Haipeng","middleName":"","lastName":"Lin","suffix":""},{"id":494482309,"identity":"3b63a5f4-cf31-410e-8014-07d650ad9b5d","order_by":2,"name":"Xiaolong Wu","email":"","orcid":"","institution":"University of Bath","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Wu","suffix":""},{"id":494482310,"identity":"b4f4d66e-ef23-46a9-b079-06c3d2a25ff1","order_by":3,"name":"Dingguo Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACewb+ZyBazoCBgQ0ixENAi2EDQxqINjZgYCZSi8EBiJbEDaRoOfbg547a9O3s5489YKixYzA4c4CQFsZ2w94zx3N39iSzGzAcS2YwONtASAszmwRv27HcDQeS2SQY2A4wGJwn6DA2Nsm/bcfSDc4/Bmr5R5QWHjZp3raaBIMbQFsY2w4QdphhM1CLbNsBw50zHptJJPYl80gS8r49e/8zybdtdfLm/InPJD58s5PjO5NAwGXMYPIwhJNAOFbgoI5YhaNgFIyCUTASAQCdpEHG95i0yAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Bath","correspondingAuthor":true,"prefix":"","firstName":"Dingguo","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-16 14:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6906475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6906475/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30168-1","type":"published","date":"2025-12-05T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97723971,"identity":"10bf7ae9-df87-48c2-82fd-8d238ed664a8","added_by":"auto","created_at":"2025-12-08 16:10:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1775188,"visible":true,"origin":"","legend":"","description":"","filename":"IntegratingActiveBrainComputerInterfacesaBCIswithPassiveBCIspBCIsunderDifferentFrustrationLevelsSR7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6906475/v1_covered_a5df7b06-e082-44e0-82d8-cf0db4c2674c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Active Brain-Computer Interfaces (aBCIs) with Passive BCIs (pBCIs) under Different Frustration Levels","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6906475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6906475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The mental state of the users can significantly affect the performance of active brain-computer interfaces (aBCIs). In this work, we aim to adopt passive BCIs (pBCIs) to measure a typical mental state, frustration, that is much relevant to aBCIs. A novel paradigm has been developed that combines both aBCIs and pBCIs under different frustration levels of users. The aBCI in this work is based on classic binary motor imagery (MI). In experiments, a new strategy was implemented that uses visual feedback to induce different levels of frustration. The electroencephalography (EEG) data collected were used for both aBCIs and pBCIs. The pBCI was utilized to assess the frustration level during the aBCI tasks, and the aBCI classification models for different levels of frustration were trained. For pBCI, the filter bank common spatial pattern (FBCSP) feature extraction and support vector machine (SVM) classification were utilized to classify three (i.e., low, moderate, high) frustration levels. For aBCI, the same method (FBCSP+SVM) was used to classify left versus right MI. We also aim to improve the performance of aBCIs in such conditions, so we developed two new methods to incorporate the pBCI results to adapt three MI classifiers to the varying states of frustration. Compared to the conventional approach of directly classifying MI tasks without considering frustration, the two proposed methods has increased the mean classification accuracy by 7.52\\% and 8.62\\%, respectively. 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