ERP Insights and Truncated SVD in conjunction with Dual-Tree Complex Wavelet Transform and Multi-View Hypergraph Neural Networks for Cognitive Distortion Analysis

ERP Insights and Truncated SVD in conjunction with Dual-Tree Complex Wavelet Transform and Multi-View Hypergraph Neural Networks for Cognitive Distortion Analysis | 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 ERP Insights and Truncated SVD in conjunction with Dual-Tree Complex Wavelet Transform and Multi-View Hypergraph Neural Networks for Cognitive Distortion Analysis Banupriya N, Neelakandan Subramani, M. Prakash, Velmurgan S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7090487/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Multi-modal EEG data analysis requires sophisticated methods for accurate prediction in the critical area of cognitive depression study in neuroscience. With the help of Multi-View Hypergraph Neural Networks (MV-HGNN) and Dual-Tree Complex Wavelet Transform (DT-CWT), a novel framework for enhancing cognitive distortion analysis is provided today. The initial stage of the procedure, DT-CWT, captures EEG signals and extracts the crucial frequency characteristics (gamma, delta, theta, beta, and alpha). Truncated singular value decomposition, or SVD, thereby reduces noise while preserving significant features. To identify task-related cognitive responses, Event-Related Potential (ERP) is used. The data is arranged into a multi-view framework following processing, which records multiple perspectives such as task-specific responses, frequency patterns, and temporal trends. To enable MV-HGNN to recognize complex cognitive patterns, a hypergraph is then constructed to mimic the complex relationships between various viewpoints. The final category predicts cognitive distortion. According on experimental data, the proposed method outperforms traditional deep learning models and delivers improved accuracy. This work shows that integrating multi-resolution feature extraction, dimensionality reduction, and hypergraph learning is effective for EEG-based cognitive distortion analysis. MV-HGNN EEG Cognitive depression DT-CWT Event-Related Potential (ERP) and Singular Value Decomposition(SVD) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Aug, 2025 Reviews received at journal 19 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviews received at journal 31 Jul, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 21 Jul, 2025 Submission checks completed at journal 16 Jul, 2025 First submitted to journal 10 Jul, 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. 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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-7090487","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492682639,"identity":"a4e018fe-df23-4633-862a-0e281f5c73cc","order_by":0,"name":"Banupriya N","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACCQglx8DABhVhJlKLMelaEhvgWggBc+neAww//tikb592LPHBDwY7eQZ23gN4tVjOOZfA2NuWljvndtphwx6GZMMGZr4EvFoMbuQYMDM2HM6dIZ3eJg30SAIDM48BYS0Mf/6nS0int/9mYKgnVgvbgQQJ6bRjwLA6TISWO+cSDva2JRvOkE5LluwxOG7YRlDL7d6DD378sZMH2mL44UdFtTw//xn8WhgYeBgOIJnAQEz08BBWMgpGwSgYBSMcAACu5TrBesiTmgAAAABJRU5ErkJggg==","orcid":"","institution":"R.M.K Engineering College","correspondingAuthor":true,"prefix":"","firstName":"Banupriya","middleName":"","lastName":"N","suffix":""},{"id":492682640,"identity":"76afdbef-daf6-4c8a-b0a7-8048b730ede8","order_by":1,"name":"Neelakandan Subramani","email":"","orcid":"","institution":"R.M.K Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Neelakandan","middleName":"","lastName":"Subramani","suffix":""},{"id":492682641,"identity":"e289975a-d30d-4dc6-b972-bb81f876b9e2","order_by":2,"name":"M. 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With the help of Multi-View Hypergraph Neural Networks (MV-HGNN) and Dual-Tree Complex Wavelet Transform (DT-CWT), a novel framework for enhancing cognitive distortion analysis is provided today. The initial stage of the procedure, DT-CWT, captures EEG signals and extracts the crucial frequency characteristics (gamma, delta, theta, beta, and alpha). Truncated singular value decomposition, or SVD, thereby reduces noise while preserving significant features. To identify task-related cognitive responses, Event-Related Potential (ERP) is used. The data is arranged into a multi-view framework following processing, which records multiple perspectives such as task-specific responses, frequency patterns, and temporal trends. To enable MV-HGNN to recognize complex cognitive patterns, a hypergraph is then constructed to mimic the complex relationships between various viewpoints. The final category predicts cognitive distortion. 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