EEG-Based Motor Imagery Classification via Multi-scale Convolutional Network and Improved Capsule Network

EEG-Based Motor Imagery Classification via Multi-scale Convolutional Network and Improved Capsule Network | 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 EEG-Based Motor Imagery Classification via Multi-scale Convolutional Network and Improved Capsule Network Biao Wang, Lei Wang, Wenchang Xu, Hanbin Ren, Wenbo Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4754637/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 Electroencephalogram (EEG) based motor imagery classification is a crucial component of brain-computer interfaces (BCIs). Traditionally, convolutional neural networks (CNNs) have been extensively employed for this task. However, due to local feature learning mechanism, CNNs have difficulty in capturing the global contextual information from EEG signal, which limit the performance of brain signal decoding. In this study, in order to overcome the shortcomings of CNNs, we introduce an improved capsule network to effectively learn various properties within EEG features and characterize intrinsic relationships between EEG features, achieving more robust performance. A novel end-to-end model is proposed in this paper which integrates muti-scale convolutional network and improved capsule network with self-attention routing mechanism, namely MSC-CapsNet. In the proposed model, a multi-scale convolutional network is employed to fully learn spatial and temporal information from EEG signals and encode them into discriminative features, then a improved capsule network with self-attention routing mechanism is applied to convert EEG features into entities corresponding to motor imagery classes and output classification results. The proposed model achieves the state-of-the-art performance on public Competition \uppercase\expandafter{\romannumeral4}-2a dataset without using any data augmentation operations, with average accuracy of 86.1%. This study demonstrates the great potential of capsule network in EEG decoding and is expected to become a general architecture to improve robustness and generalization capabilities. Brain-computer interface (BCI) motor imagery (MI) electroencephalography (EEG) Attention deep learning (DL) convolution neural network (CNN) capsule network (CapsNet) 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-4754637","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338679258,"identity":"7a2b53b6-9f7c-45eb-86e7-698bab61cb26","order_by":0,"name":"Biao Wang","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Biao","middleName":"","lastName":"Wang","suffix":""},{"id":338679259,"identity":"a00f2b47-8a63-441f-925b-d5282abfdb50","order_by":1,"name":"Lei Wang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":338679260,"identity":"d0096c10-3e58-43da-824e-3ec2b9192191","order_by":2,"name":"Wenchang Xu","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wenchang","middleName":"","lastName":"Xu","suffix":""},{"id":338679261,"identity":"c3489ade-b5b0-4285-9822-42f9c33688e1","order_by":3,"name":"Hanbin Ren","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hanbin","middleName":"","lastName":"Ren","suffix":""},{"id":338679262,"identity":"2061189b-ffd3-4408-a472-65a1f4d9c458","order_by":4,"name":"Wenbo Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACCTB5QI6BmYdELcZgLQdI0ZLYwECsFv7Zzccefm27kz6/nffg4w8MdvIM7Gfx65S4cyzdWLbtWe6Gw3zJBgcYkg0bePIS8GoxkMgxk5bcdjh3AzOPmcQBBuYEBgkeAwJa8r+BtKTLN/OY/zjAUE+Mlhw2yY/bDicwHOYxA3r/MGEtEjfSzKQZ/z0zBPlF4ozBccM2nhz8WvhnJD+T/HHmjrx8/9mDHyoqquX52c/g1wICSPEOVMxGUD0QMP4gRtUoGAWjYBSMXAAA8qJCVu/bLisAAAAASUVORK5CYII=","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Wenbo","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2024-07-17 08:15:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4754637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4754637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62253045,"identity":"4af03fba-b8ff-498c-b135-c6b011037cd7","added_by":"auto","created_at":"2024-08-12 06:42:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1187081,"visible":true,"origin":"","legend":"","description":"","filename":"cognitivecomputationsubmit2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4754637/v1_covered_d3bf5274-1203-457d-9a04-ef8e3727d22c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"EEG-Based Motor Imagery Classification via Multi-scale Convolutional Network and Improved Capsule Network","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":"Brain-computer interface (BCI), motor imagery (MI), electroencephalography (EEG), Attention, deep learning (DL), convolution neural network (CNN), capsule network (CapsNet)","lastPublishedDoi":"10.21203/rs.3.rs-4754637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4754637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Electroencephalogram (EEG) based motor imagery classification is a crucial component of brain-computer interfaces (BCIs). Traditionally, convolutional neural networks (CNNs) have been extensively employed for this task. However, due to local feature learning mechanism, CNNs have difficulty in capturing the global contextual information from EEG signal, which limit the performance of brain signal decoding. In this study, in order to overcome the shortcomings of CNNs, we introduce an improved capsule network to effectively learn various properties within EEG features and characterize intrinsic relationships between EEG features, achieving more robust performance. A novel end-to-end model is proposed in this paper which integrates muti-scale convolutional network and improved capsule network with self-attention routing mechanism, namely MSC-CapsNet. 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