Classifying native versus foreign speech perception from EEG using linguistic speech features

Classifying native versus foreign speech perception from EEG using linguistic speech features | 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 Classifying native versus foreign speech perception from EEG using linguistic speech features Corentin Puffay, Jonas Vanthornhout, Marlies Gillis, Pieter De Clercq, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4269902/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract When a person listens to natural speech, the relation between features of the speech signal and the corresponding evoked electroencephalogram (EEG) is indicative of neural processing of the speech signal. Using linguistic representations of speech, we investigate the differences in neural processing between speech in a native and foreign language that is not understood. We conducted experiments using three stimuli: a comprehensible language, an incomprehensible language, and randomly shuffled words from a comprehensible language, while recording the EEG signal of native Dutch-speaking participants. We modeled the neural tracking of linguistic features of the speech signals using a deep-learning model in a match-mismatch task that relates EEG signals to speech, while accounting for lexical segmentation features reflecting acoustic processing. The deep learning model effectively classifies languages. We also observed significant differences in tracking patterns between comprehensible and incomprehensible speech stimuli within the same language. It demonstrates the potential of deep learning frameworks in measuring speech understanding objectively. EEG decoding deep learning CNN linguistics Full Text Additional Declarations No competing interests reported. Supplementary Files Appendix.pdf Cite Share Download PDF Status: Published Journal Publication published 14 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Jun, 2024 Reviews received at journal 11 Jun, 2024 Reviews received at journal 28 May, 2024 Reviewers agreed at journal 06 May, 2024 Reviewers agreed at journal 02 May, 2024 Reviewers agreed at journal 01 May, 2024 Reviewers invited by journal 29 Apr, 2024 Editor assigned by journal 29 Apr, 2024 Editor invited by journal 29 Apr, 2024 Submission checks completed at journal 29 Apr, 2024 First submitted to journal 15 Apr, 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. 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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-4269902","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":298012603,"identity":"2a4bfd56-ec0d-465a-bca5-a824f9c7dde5","order_by":0,"name":"Corentin 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