Reconstructing damaged fNIRS signals with a generative deep learning model

Reconstructing damaged fNIRS signals with a generative deep learning model | 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 Reconstructing damaged fNIRS signals with a generative deep learning model Yingxu Zhi, Baiqiang Zhang, Bingxin Xu, Fei Wan, Haijing Niu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4807209/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Dec, 2024 Read the published version in Artificial Intelligence Review → Version 1 posted 11 You are reading this latest preprint version Abstract Functional near-infrared spectroscopy (fNIRS) technology offers a promising avenue for assessing brain function across participant groups. Despite its numerous advantages, the fNIRS technique often faces challenges such as noise contamination and motion artifacts from data collection. Methods for improving fNIRS signal quality are urgently needed, especially with the development of wearable fNIRS equipment and corresponding applications in natural environments. To solve these issues, we propose a generative deep learning approach to recover damaged fNIRS signals from one or more measurement channels. The model could capture spatial and temporal variations in the time series of fNIRS data by integrating multiscale convolutional layers, gated recurrent units (GRUs), and linear regression analyses. Several extensive experiments were conducted on a dataset of healthy elderly individuals to assess the performance of the model. Collectively, the results demonstrate that the proposed model can accurately reconstruct damaged time series for individual channels while preserving intervariable relationships. Under two simulated scenarios of multichannel damage, the model maintains robust reconstruction accuracy and consistency in terms of functional connectivity. Our findings underscore the potential of generative deep learning techniques in reconstructing damaged fNIRS signals, offering a novel perspective for accurate data provision in clinical diagnosis and brain research. fNIRS Signals Reconstruction Generative Learning Multi-scale Temporal Model Time Series Prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Dec, 2024 Read the published version in Artificial Intelligence Review → Version 1 posted Editorial decision: Revision requested 25 Sep, 2024 Reviews received at journal 24 Sep, 2024 Reviews received at journal 23 Sep, 2024 Reviews received at journal 18 Sep, 2024 Reviewers agreed at journal 08 Sep, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers invited by journal 29 Aug, 2024 Editor assigned by journal 08 Aug, 2024 Submission checks completed at journal 31 Jul, 2024 First submitted to journal 26 Jul, 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. 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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-4807209","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":340992067,"identity":"92020328-c512-4a1e-bbe3-5944991028ef","order_by":0,"name":"Yingxu Zhi","email":"","orcid":"","institution":"State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yingxu","middleName":"","lastName":"Zhi","suffix":""},{"id":340992068,"identity":"09345162-1c7b-4918-a4fd-63a06a8abf84","order_by":1,"name":"Baiqiang Zhang","email":"","orcid":"","institution":"State Key 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