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SSVEPs were recorded for sawtooth luminance increments and decrements at 2.73 Hz and modeled with logistic regressions trained on data from 98 adults with glaucoma and 71 controls. Results show phase features outperform amplitude-only features, with OFF advantages appearing mainly when trained on amplitude and not persisting after low-SNR eye removal. The study emphasizes the clinical relevance of visual signal delay estimates for future diagnostic tools.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/glaucoma-classification-through-ssvep-derived-on-and-off-pathway-features-research/457877/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/glaucoma-classification-through-ssvep-derived-on-and-off-pathway-features-research/457877.png","ImageObject",300,407,{"name":92,"@type":93},"dawugda","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the main purpose of the study?","Question",{"text":112,"@type":113},"To assess how the amplitude and phase of ON- and OFF-pathway biased SSVEPs contribute to classifying glaucoma patients versus healthy controls.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were ON- and OFF-pathway SSVEPs elicited in the experiment?",{"text":117,"@type":113},"SSVEPs were recorded using sawtooth luminance increments for ON-biasing and sawtooth luminance decrements for OFF-biasing at a temporal frequency of 2.73 Hz.",{"name":119,"@type":110,"acceptedAnswer":120},"Which feature type performed better for classification?",{"text":121,"@type":113},"Classification using signal phase features significantly outperformed classification using only signal amplitude features.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},457877,1791093558,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},3985747870947,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Glaucoma  \nGlaucoma Classification Through SSVEP-Derived ON-and OFF-Pathway Features  \nMartin T. W. Scott 1,*, Hui Xu2,*, Alexandra Yakovleva3, Robert Tibshirani2, Jeffrey L. Goldberg3, and Anthony M. Norcia 1,3  \n1 Department of Psychology, Stanford University, Stanford, CA, USA  \n2 Department of Statistics, Stanford University, Stanford, CA, USA  \n3 Department of Ophthalmology, Spencer Center for Vision Research, Byers Eye Institute, Stanford University, Stanford, CA, USA  \nCorrespondence: Martin Scott, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, [USA. e-mail:](USA. e-mail: martin.scott2@ucsf.edu)[ martin.scott2@ucsf.edu](USA. e-mail: martin.scott2@ucsf.edu)  \nReceived: March 17, 2025  \nAccepted: October 1, 2025  \nPublished: January 5, 2026  \nKeywords: glaucoma; ON/OFF pathways; SSVEP; phase delay; machine learning classification  \nCitation: Scott MTW, Xu H, Yakovleva A, Tibshirani R, Goldberg JL, Norcia AM. Glaucoma classification through SSVEP-derived ON-and  \nOFF-pathway features. Transl Vis Sci Technol. 2026;15(1):2, [https://doi.org/10.1167/tvst.15.1.2](https://doi.org/10.1167/tvst.15.1.2)  \nPurpose: This work aims to evaluate the relative contribution of the amplitude and phase of both ON- and OFF-pathway biased steady-state visually evoked potentials (SSVEPs) to the classification of patients with glaucoma from healthy controls.  \nMethods: SSVEPs were recorded for sawtooth luminance increments (ON-biasing) and decrements (OFF-biasing), modulating at a temporal frequency of 2.73 Hz. SSVEP data from 98 adults with glaucoma and 71 controls were used to train a set of logistic regressions. Data were partitioned prior to training to investigate the relative contribution to classification for amplitude and phase features derived from ON-versus OFF-pathway stimulation.  \nResults: We report moderate overall classification accuracy (area under the curve ∼0.7) . Classification based solely on signal phase features significantly outperformed classification based solely on signal amplitude features. Classification using OFF-pathway biasing features produced a statistically significant improvement in classification only when training on signal amplitude features. This OFF advantage was not conserved in adataset with low signal-to-noise eyes removed.  \nConclusions: Our findings highlight the informational value of signal phase, a metric often omitted in applications of the SSVEP to glaucoma and other optic neuropathies. Additionally, our results suggest that OFF-pathway amplitude features may be less vulnerable to the limitations imposed by a low signal-to-noise ratio. However, they are not indicative of a gross difference in glaucoma classification performance between ON-and OFF-pathway biased features.  \nTranslational Relevance: Electrophysiological estimates of visual signal delay should be considered in future clinical diagnostic tools as they make a material contribution to the classification of glaucomatous eyes.  \nIntroduction  \nMultiple electrophysiological studies have performed individual patient classification using the isolated-check visually evoked potential (icVEP)—a form of steady-state visually evoked potential (SSVEP) .1–10 The icVEP protocol has variations that are designed to bias neural responses (measured atthe scalp) toward either the ON or OFF pathway. This biasing is accomplished by presenting a rectilinear grid of small patches that sinusoidally modulate  \ntheir luminance either above the luminance of an otherwise uniform background (ON-pathway favoring) or below it (OFF-pathway favoring) . Recent work using this paradigm in glaucoma has focused on ONfavoring stimuli. This choice was based on early grouplevel and individual-level analyses 1 , 11 showing greater effects of glaucoma (and best classification performance) with ON-biasing sinusoidal stimulation. Using a simple icVEP signal-to-noise ratio (SNR) threshold, the neural response evoked by this stimulus can reportedly separate pati","cbCaijrlWyNdlMov","https://ap.wps.com/l/cbCaijrlWyNdlMov","pdf",2130484,13,"English","# Introduction\n## Objective and purpose\n# Methods\n## SSVEP recording and stimulus design\n## Data partitioning and logistic regression\n# Results\n## Classification accuracy and feature comparisons\n## OFF-pathway advantage and robustness checks\n# Conclusions\n## Informational value of phase and clinical relevance","[{\"question\":\"What was the main purpose of the study?\",\"answer\":\"To assess how the amplitude and phase of ON- and OFF-pathway biased SSVEPs contribute to classifying glaucoma patients versus healthy controls.\"},{\"question\":\"How were ON- and OFF-pathway SSVEPs elicited in the experiment?\",\"answer\":\"SSVEPs were recorded using sawtooth luminance increments for ON-biasing and sawtooth luminance decrements for OFF-biasing at a temporal frequency of 2.73 Hz.\"},{\"question\":\"Which feature type performed better for classification?\",\"answer\":\"Classification using signal phase features significantly outperformed classification using only signal amplitude features.\"}]","Glaucoma Classification Through SSVEP-Derived ON-and OFF-Pathway Features - Research | PDF",1790750751,33]