Disorder Quantifier Outperforms Entropy in EEG Classification of Motor and Visual States

Disorder Quantifier Outperforms Entropy in EEG Classification of Motor and Visual States | 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 Disorder Quantifier Outperforms Entropy in EEG Classification of Motor and Visual States Jorge Silveira, Iara Ferré, John Araujo, Gustavo Zampier Dos Santos Lima, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8862298/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Feature selection is critical for the performance of Machine Learning (ML) algorithms, as model outcomes rely heavily on input data quality. This study empirically validates two recurrence analysis quantifiers microstate entropy and disorder as discriminative features for EEG-based classification. We analyzed EEG data from individuals across four experimental conditions (resting and cycling, with eyes open or closed) to characterize cortical dynamics in distinct motor and sensory states. Using a Random Forest classifier, we compared the performance of microstate entropy and disorder. Our results demonstrate that cycling significantly reduces cortical complexity, corroborating recent findings. Notably, the disorder quantifier (specifically for microstate size N =4) outperformed entropy, achieving classification accuracies of up to 82% for visual states during movement and 79% during rest. Furthermore, we observed hemispheric asymmetries between parietal and occipital regions and confirmed that integrating data from all eight EEG channels substantially improves classification compared to single-channel analysis. These findings highlight the disorder quantifier as a robust, compact, and interpretable descriptor of cortical dynamics. Biological sciences/Computational biology and bioinformatics Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8862298","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":598630430,"identity":"e426cef3-8501-4e16-b54a-49a12e0c36ba","order_by":0,"name":"Jorge Silveira","email":"","orcid":"","institution":"Federal University of Paraná","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Silveira","suffix":""},{"id":598630431,"identity":"127da96c-2584-4081-aa91-c3ce4d686dda","order_by":1,"name":"Iara Ferré","email":"","orcid":"","institution":"Universidade Federal do Rio Grande do Norte, Natal","correspondingAuthor":false,"prefix":"","firstName":"Iara","middleName":"","lastName":"Ferré","suffix":""},{"id":598630432,"identity":"151554bc-9325-4c2d-b50f-839b596e03f5","order_by":2,"name":"John Araujo","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Araujo","suffix":""},{"id":598630433,"identity":"c67810a2-2518-4784-8219-c19f68253d4c","order_by":3,"name":"Gustavo Zampier Dos Santos Lima","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYNACAwYGfuaDDQwJIA7zAQYGxgYitEi2JUK1sCUQowWk61gClEVIi7z/4aebCwps8o2PMTd/eFBjB9TL++wB4457OLUYHjhmdnuGQZrltmOMbRIJx5KBWtjNDRjPFOPW0thgdpvH4LCB2f3GNobEBmYGg/ttbBKMbQm4tTSzfwNq+W9g3MbY/CGxoR5oCxt+LfJsPCBbDhgYsDE2SCQ2HCasxYCHpwzol2QDCYhfjvNIgrQknsFjS//xbbcL/tgZ8LexP/74o6Zajg+k5eMOPLYcAEY3sgAPmMStAWhLA5qWUTAKRsEoGAUYAAD8eVBcDbGByAAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":true,"prefix":"","firstName":"Gustavo","middleName":"Zampier Dos Santos","lastName":"Lima","suffix":""},{"id":598630434,"identity":"4aacc039-35d0-412d-a5cc-604b34c72730","order_by":4,"name":"Gilberto Corso","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":false,"prefix":"","firstName":"Gilberto","middleName":"","lastName":"Corso","suffix":""},{"id":598630435,"identity":"8ba1fac1-6f76-4358-95ca-a9b28ee470f9","order_by":5,"name":"Sergio Lopes","email":"","orcid":"","institution":"Federal University of Paraná","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Lopes","suffix":""},{"id":598630436,"identity":"ad77c18b-43a6-4b2f-a2a7-1df50660d096","order_by":6,"name":"Thiago Prado","email":"","orcid":"","institution":"Federal University of Paraná","correspondingAuthor":false,"prefix":"","firstName":"Thiago","middleName":"","lastName":"Prado","suffix":""}],"badges":[],"createdAt":"2026-02-12 12:54:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8862298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8862298/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107127435,"identity":"74b3e876-2f2f-4dcf-8097-4c48d0991043","added_by":"auto","created_at":"2026-04-17 06:11:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3712821,"visible":true,"origin":"","legend":"","description":"","filename":"DisorderQuantifierOutperformsEntropyinEEGClassificationofMotorandVisualStates5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8862298/v1_covered_4b4b4456-da29-471b-a7e5-a93e8f62dbdf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disorder Quantifier Outperforms Entropy in EEG Classification of Motor and Visual States","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8862298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8862298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFeature selection is critical for the performance of Machine Learning (ML) algorithms, as model outcomes rely heavily on input data quality. This study empirically validates two recurrence analysis quantifiers microstate entropy and disorder as discriminative features for EEG-based classification. We analyzed EEG data from individuals across four experimental conditions (resting and cycling, with eyes open or closed) to characterize cortical dynamics in distinct motor and sensory states. Using a Random Forest classifier, we compared the performance of microstate entropy and disorder. Our results demonstrate that cycling significantly reduces cortical complexity, corroborating recent findings. Notably, the disorder quantifier (specifically for microstate size \u003cem\u003eN\u003c/em\u003e=4) outperformed entropy, achieving classification accuracies of up to 82% for visual states during movement and 79% during rest. Furthermore, we observed hemispheric asymmetries between parietal and occipital regions and confirmed that integrating data from all eight EEG channels substantially improves classification compared to single-channel analysis. These findings highlight the disorder quantifier as a robust, compact, and interpretable descriptor of cortical dynamics.\u003c/p\u003e","manuscriptTitle":"Disorder Quantifier Outperforms Entropy in EEG Classification of Motor and Visual States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 06:16:30","doi":"10.21203/rs.3.rs-8862298/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c72147af-c9fb-4fcf-9a68-d9453ccd46d4","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63706644,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":63706645,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-17T06:10:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 06:16:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8862298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8862298","identity":"rs-8862298","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}