A Graph-Discriminative Low-Rank Embedding Approach for Fusion of EEG and Eye Tracking in Depression Recognition

A Graph-Discriminative Low-Rank Embedding Approach for Fusion of EEG and Eye Tracking in Depression Recognition | 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 A Graph-Discriminative Low-Rank Embedding Approach for Fusion of EEG and Eye Tracking in Depression Recognition Wu Sun, Xin Zhang, Dongming Fan, Xiuzhen Wang, Linping Yang, Jingfeng Gou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9005389/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Major depressive disorder (MDD) is a prevalent psychiatric illness, but its diagnosis still heavily relies on subjective scales and clinical judgment, leading to inconsistencies in assessments and limited capabilities for early detection. EEG and eye tracking (ET) provide complementary neurobehavioral insights, but integrating these heterogeneous signals into stable and interpretable multimodal feature representations remains a challenge. To address this issue, we propose the Graph Discriminative Low Rank Correlation Embedding (GDLRCE) framework. This framework combines low-rank representation learning, graph-based discriminative constraints, and correlation analysis within a unified optimization model to fuse EEG and ET features while minimizing redundancy. Three visual oculomotor paradigms including prosaccade (PS), antisaccade (AS), and fixation stability (FS) were designed to examine perceptual processing, executive control, and sustained attention, respectively. EEG and ET features extracted from each paradigm were projected into a shared low rank subspace that preserves both local structural information and class discriminability. Experimental results demonstrate that GDLRCE outperforms unimodal and conventional fusion approaches in depression recognition, while revealing interpretable multistage brain-eye coupling patterns. These findings highlight the potential of a robust multimodal framework for automatic MDD identification and mechanism-oriented analysis. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Biological sciences/Neuroscience Major depressive disorder (MDD) EEG–eye tracking fusion Low rank representation Graph embedding Multimodal fusion Neurobehavioral biomarkers Full Text Additional Declarations No competing interests reported. Supplementary Files figures.zip figures.zip supplementary.zip supplementary.zip Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 30 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Editor invited by journal 10 Mar, 2026 Submission checks completed at journal 07 Mar, 2026 First submitted to journal 07 Mar, 2026 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-9005389","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":630279967,"identity":"97f86a8d-e2b6-48a6-81fa-fd54c9b7a99f","order_by":0,"name":"Wu Sun","email":"","orcid":"","institution":"Mental Health Center of Guangyuan City","correspondingAuthor":false,"prefix":"","firstName":"Wu","middleName":"","lastName":"Sun","suffix":""},{"id":630279968,"identity":"0f9e8459-fecc-41c9-b763-26887460bd25","order_by":1,"name":"Xin Zhang","email":"","orcid":"","institution":"Mental Health Center of Guangyuan 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