[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122938-en":3,"doc-seo-122938-105":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":4,"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},122938,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Classification of attention performance postlongitudinal tDCS via functional connectivity and machine learning methods","Attention enables selective processing by filtering irrelevant stimuli. Evidence on how long-term interventions such as transcranial direct current stimulation (tDCS) alter attention remains limited. This study classifies post-tDCS attention performance using functional connectivity metrics combined with machine learning. Fifty participants completed an attention task with EEG on Day 1, received 1 mA or sham tDCS from Day 2–8, and repeated the task on Day 10. Adaboost with recursive feature elimination achieved 91.84% accuracy, informing neurofeedback assessment frameworks.","Classification of attention performance postlongitudinal tDCS via functional connectivity and  \nmachine learning methods  \nAkash K Rao Applied Cognitive Science Laboratory  \nIndian Institute of Technology Mandi Mandi, India 0000-0003-4025-1042  \nVishnu K Menon Applied Cognitive Science Laboratory  \nIndian Institute of Technology Mandi Mandi, India 0009-0007-9449-0934  \nArnav Bhavsar School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi, India [arnav@iitmandi.ac.in](arnav@iitmandi.ac.in)  \nShubhajit Roy Chowdhury School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi, India[src@iitmandi.ac.in](src@iitmandi.ac.in)  \nRamsingh Negi Cognitive Control and Machine learning group Institute of Nuclear Medicine and Allied Sciences Delhi, India [ramsingh@inmas.gov.in](ramsingh@inmas.gov.in)  \nAbstract—Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following longterm interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention.  \nKeywords—Functional connectivity, transcranial direct current stimulation, phase synchronization, dynamic causal modeling, wavelet coherence, Electroencephalography, Adaboost  \nI. INTRODUCTION  \nAttention is a limited resource that allows us to focus on and employ cognitive capabilities to certain stimuli, enabling us to filter out extraneous information and prioritize what is essential [1] . Selective attention is the capacity to fixate on one stimulus while disregarding others. Divided attention, on the other hand, refers to the ability to devote attention to various things simultaneously, albeit at the expense of performance [1] . Technological advancements have shed light on the neurological foundations of attention mechanisms in recent  \nVarun Dutt Applied Cognitive Science  \nLaboratory Indian Institute of Technology  \nMandi  \nMandi, India [varun@iitmandi.ac.in](varun@iitmandi.ac.in)  \nyears. Functional neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have revealed information about the brain regions engaged in attentional activities. [1] The frontal cortex, particularly the anterior cingulate cortex (ACC) and the prefrontal cortex (PFC) is critical for attentional regulation, conflict monitoring, and goal-directed behavior orchestration. The parietal cortex, specifically the intraparietal sulcus (IPS), is involved in spatial attention orientation, allowing us to focus on specific regions in our visual field [1] .  \nIn the last few years, there has been a surge in interest in testing the efficacy of different methods to improve attention [2] . Non-invasive brain stimulation techniques like tDCS have become popular in the last decade. By delivering mild electrical currents across specific brain regions, tDCS modulates cortical excitability, eventually influencing neuronal activity. Investigations have revealed that tDCS applied over the dorsolateral prefrontal cortex (DLPFC), a crucial brain region modulating attentional mechanisms, increased ","cbCaieOhcnwmBsKC","https://ap.wps.com/l/cbCaieOhcnwmBsKC","pdf",361983,1,6,"English","en",105,"# Introduction\n# Methods\n## Participants and experimental design\n## EEG acquisition and attention task\n## tDCS protocol\n# Feature extraction and modeling\n## Functional connectivity metrics\n## Machine learning classifiers\n## Recursive feature elimination\n# Results\n## Classification accuracy and findings\n# Discussion\n## Implications for neurofeedback frameworks","[{\"question\":\"How was attention performance evaluated after longitudinal tDCS?\",\"answer\":\"Participants performed an attention task with EEG on Day 1, received 1 mA or sham tDCS from Day 2 to Day 8, and repeated the same task on Day 10 to measure post-intervention performance.\"},{\"question\":\"Which analytical approach was used to classify attention performance?\",\"answer\":\"Functional connectivity metrics derived from EEG were used as inputs to multiple machine learning methods to classify attention performance.\"},{\"question\":\"What classification accuracy did the best model achieve?\",\"answer\":\"The combination of the Adaboost model and recursive feature elimination produced a classification accuracy of 91.84%.\"}]","Classification of attention performance postlongitudinal tDCS via functional connectivity and machine learning methods | PDF",1785813780,15,{"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},"classification-of-attention-performance-postlongitudinal-tdcs-via-functional-connectivity-and-machine-learning-methods","",{"@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/classification-of-attention-performance-postlongitudinal-tdcs-via-functional-connectivity-and-machine-learning-methods/122938/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How was attention performance evaluated after longitudinal tDCS?","Question",{"text":75,"@type":76},"Participants performed an attention task with EEG on Day 1, received 1 mA or sham tDCS from Day 2 to Day 8, and repeated the same task on Day 10 to measure post-intervention performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which analytical approach was used to classify attention performance?",{"text":80,"@type":76},"Functional connectivity metrics derived from EEG were used as inputs to multiple machine learning methods to classify attention performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What classification accuracy did the best model achieve?",{"text":84,"@type":76},"The combination of the Adaboost model and recursive feature elimination produced a classification accuracy of 91.84%.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"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"]