An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection

An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection | 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 Research Article An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection Dan-Ting Duan, Bing Sun, Qiang Yang, Long Ye, Qin Zhang, Jun Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4626107/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Memetic Computing → Version 1 posted 4 You are reading this latest preprint version Abstract Electroencephalogram (EEG) plays a significant role in emotion recognition because it contains abundant information. However, due to the highly correlated EEG channels, a lot of redundant EEG features exist, which not only potentially degrade the emotion recognition accuracy, but also bring high computational cost. To address this challenge, this paper proposes an adaptive matrix-based evolutionary computation (MEC) framework to select as a small number of informative EEG features as possible for effective emotion recognition. Unlike most existing EC algorithms that utilize vector-based operations, this framework leverages matrix-based operations to reduce feature redundancy and improve classification accuracy by dynamically adjusting the feature subset size according to the characteristics of the dataset. In such a way, the selection efficiency is largely improved. To verify the effectiveness and efficiency of this framework, the classical Genetic Algorithm (GA), the typical Particle Swarm Optimization (PSO) algorithm, and the classical Differential Evolution (DE) algorithm, are respectively embedded into this framework for EEG feature selection, and then evaluated on three widely used public EEG datasets for emotion recognition. Compared with several state-of-the-art EEG feature selection algorithms, the devised framework is much more effective in terms of the classification accuracy, the feature subset optimization, and the computational efficiency. In addition, the experimental results further reveal that the selected feature subsets are very different for different genders. This indicates the demand of gender-sensitive EEG feature selection for emotion recognition. EEG feature selection matrix-based evolutionary computation emotion recognition Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Memetic Computing → Version 1 posted Editorial decision: Revision requested 22 Jul, 2024 Editor assigned by journal 15 Jul, 2024 Submission checks completed at journal 24 Jun, 2024 First submitted to journal 23 Jun, 2024 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-4626107","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327205127,"identity":"181c3ec5-998d-4b6a-b492-15f6d70bdfc4","order_by":0,"name":"Dan-Ting Duan","email":"","orcid":"","institution":"Communication University of China","correspondingAuthor":false,"prefix":"","firstName":"Dan-Ting","middleName":"","lastName":"Duan","suffix":""},{"id":327205128,"identity":"20b4f630-d282-4695-b5d9-284a4c3a602d","order_by":1,"name":"Bing Sun","email":"","orcid":"","institution":"Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Sun","suffix":""},{"id":327205129,"identity":"dca52c6e-f66e-4948-b63f-0dfe5ab1d488","order_by":2,"name":"Qiang Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAlElEQVRIiWNgGAWjYNCCCgk5fhK1nLEwlmwgSQdjW0XiBqK16E47nSbxdp4E4wYG5oePbhCjxex27jbJudskmM0Z2IyNc4jVIs27TYLNsoGHTZoELXMkeAwOkKalQUKCJC2bLecckzCQbCbBLxtvvKmpq+9nb374mCgtQMAiwQOimIlUDlb7gYcE1aNgFIyCUTACAQAmOS4J1kucfQAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Yang","suffix":""},{"id":327205130,"identity":"9ff1a60c-9096-40f7-8b9b-277e3ddbb8f3","order_by":3,"name":"Long Ye","email":"","orcid":"","institution":"Communication University of China","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Ye","suffix":""},{"id":327205131,"identity":"c00b81ce-baf6-414e-acf8-38d43188f941","order_by":4,"name":"Qin Zhang","email":"","orcid":"","institution":"Communication University of China","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Zhang","suffix":""},{"id":327205132,"identity":"4e71421d-c3ac-48de-93b4-e4f05a1024c8","order_by":5,"name":"Jun Zhang","email":"","orcid":"","institution":"Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-06-23 17:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4626107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4626107/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12293-024-00434-2","type":"published","date":"2025-01-13T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74284725,"identity":"c8e9b78b-15d3-4fff-8630-3d9022dbf116","added_by":"auto","created_at":"2025-01-20 16:11:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":825440,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptforMECv18.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4626107/v1_covered_193e6abe-7cc4-4376-aabf-c591601ff465.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection","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":"memetic-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meme","sideBox":"Learn more about [Memetic Computing](http://link.springer.com/journal/12289)","snPcode":"12293","submissionUrl":"https://submission.nature.com/new-submission/12293/3","title":"Memetic Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"EEG, feature selection, matrix-based evolutionary computation, emotion recognition","lastPublishedDoi":"10.21203/rs.3.rs-4626107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4626107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectroencephalogram (EEG) plays a significant role in emotion recognition because it contains abundant information. 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