[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125354-en":3,"doc-seo-125354-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125354,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","EEG-based characterization of auditory attention and meditation - an ERP and machine learning approach","EEG-based research investigates how meditation modulates neural responses to sound, combining EEG recordings during meditative states and an auditory oddball task. The analysis integrates event-related potentials with theta, alpha, and beta spectral power and applies machine learning to separate meditative from cognitive conditions. Results show enhanced P300 amplitude for oddball stimuli, increased frontal alpha and beta power in meditation, and reduced central theta power, alongside a Random Forest model achieving moderate differentiation using ERP and spectral biomarkers.","TYPE Clinical Trial  \nPUBLISHED 26 August 2025  \nDOI 10.3389/fnhum.2025.1616456  \nOPEN ACCESS  \nEDITED BY  \nElias Ebrahimzadeh, University of Tehran, Iran  \nREVIEWED BY  \nVignayanandam Ravindernath Muddapu, Azim Premji University, India  \nDaniel Baldauf,  \nUniversity of Trento, Italy  \n*CORRESPONDENCE  \nEyad Talal Attar  \n [etattar@kau.edu.sa](etattar@kau.edu.sa)  \nRECEIVED 23 April 2025  \nACCEPTED 21 July 2025  \nPUBLISHED 26 August 2025  \nCITATION  \nAttar ET (2025) EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.  \nFront. Hum. Neurosci. 19:1616456 .  \ndoi: 10.3389/fnhum.2025.1616456  \nCOPYRIGHT  \n© 2025 Attar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nEEG-based characterization of auditory attention and meditation: an ERP and machine learning approach  \nEyad Talal Attar*  \nDepartment of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia  \nIntroduction: This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.  \nMethods: The study utilized data from 13 participants aged 24–58, which researchers obtained through an openly accessible OpenNeuro dataset.  \nResult: Examination of eventrelated potentials (ERPs) demonstrated that P300 amplitude showed significant growth when responding to oddball stimuli, which indicates increased attention allocation (p \u003C 0.05) . Spectral power analysis demonstrated an increase in frontal alpha and beta power during meditation while central theta power decreased, which suggests reduced cognitive load and enhanced internal focus. Meditation experience showed a statistical relationship with frontal alpha power, where r = 0.45 and p \u003C 0.03. A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.  \nConclusion: The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. The study of meditation’s effects on brain activity reveals its benefits for emotional regulation and concentration improvement. The research findings deliver strong evidence that meditation induces distinct neural modifications detectable through ERP and spectral analysis. The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain– computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.  \nKEYWORDS  \nmeditation, EEG, P300, event-related potentials, spectral power, alpha, beta, cognitive tasks  \nFrontiers in Human Neuroscience 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nScientific interest has surged regarding meditation’s impact on advanced cognitive functions, including consciousness, attention, and emotional regulation. Through sustained ","cbCaidclEE8okkVK","https://ap.wps.com/l/cbCaidclEE8okkVK","pdf",3855525,1,16,"English","en",105,"# Introduction\n## Meditation and cognitive functions\n## Auditory oddball paradigm and P300\n## EEG methods: ERP and spectral analysis","[{\"question\":\"How does the study measure the effect of meditation on the brain?\",\"answer\":\"It uses EEG recordings during both meditation and an auditory oddball task, analyzing event-related potentials and spectral power (theta, alpha, beta).\"},{\"question\":\"What EEG changes are reported during meditation compared with cognitive states?\",\"answer\":\"Meditation shows increased frontal alpha and beta power and decreased central theta power, consistent with reduced cognitive load and improved internal focus.\"},{\"question\":\"How do machine learning methods contribute to the findings?\",\"answer\":\"A Random Forest classifier distinguishes meditative from cognitive states using ERP features like P300 amplitude and spectral predictors such as frontal alpha and beta power.\"}]","EEG-based characterization of auditory attention and meditation - an ERP and machine learning approach | PDF",1785898378,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"eeg-based-characterization-of-auditory-attention-and-meditation-an-erp-and-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/eeg-based-characterization-of-auditory-attention-and-meditation-an-erp-and-machine-learning-approach/125354/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study measure the effect of meditation on the brain?","Question",{"text":75,"@type":76},"It uses EEG recordings during both meditation and an auditory oddball task, analyzing event-related potentials and spectral power (theta, alpha, beta).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What EEG changes are reported during meditation compared with cognitive states?",{"text":80,"@type":76},"Meditation shows increased frontal alpha and beta power and decreased central theta power, consistent with reduced cognitive load and improved internal focus.",{"name":82,"@type":73,"acceptedAnswer":83},"How do machine learning methods contribute to the findings?",{"text":84,"@type":76},"A Random Forest classifier distinguishes meditative from cognitive states using ERP features like P300 amplitude and spectral predictors such as frontal alpha and beta power.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]