[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123867-en":3,"doc-seo-123867-105":30,"detail-sidebar-cat-0-en-105":92},{"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},123867,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods","Executive functioning supports planning, organizing, and regulating goal-directed behavior, yet longitudinal changes after transcranial direct current stimulation (tDCS) remain under-characterized. This study classifies executive functioning performance after tDCS using functional connectivity features derived from EEG and machine learning models. Fifty participants complete an executive functioning task, receive real or sham tDCS from Day 2–Day 8, and repeat testing on Day 10. A partial directed coherence plus multi-layer perceptron with recursive feature elimination achieves 95.44% accuracy, informing real-time neurofeedback for post-tDCS assessment and enhancement.","Classification of executive functioning performance post-longitudinal tDCS using functional connectivity  \nand machine 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  \nDipanshu Verma School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi, India 0000-0003-2461-5547  \nShashank Uttrani Applied Cognitive Science Laboratory  \nIndian Institute of Technology Mandi Mandi, India 0000-0003-2601-2125  \nVarun Dutt Applied Cognitive Science Laboratory  \nIndian Institute of Technology Mandi Mandi, India 0000-0002-2151-8314  \nAyushman Dixit School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi, India 0000-0002-7733-0978  \nAbstract— Executive functioning is a cognitive process that enables humans to plan, organize, and regulate their behavior in agoal-directed manner. Understanding and classifying the changes in executive functioning after longitudinal interventions (like transcranial direct current stimulation (tDCS)) has not been explored in the literature. This study employs functional connectivity and machine learning algorithms to classify executive functioning performance post-tDCS. Fifty subjects were divided into experimental and placebo control groups. EEG data was collected while subjects performed an executive functioning task on Day 1. The experimental group received tDCS during task training from Day 2 to Day 8, while the control group received sham tDCS. On Day 10, subjects repeated the tasks specified on Day 1. Different functional connectivity metrics were extracted from EEG data and eventually used for classifying executive functioning performance using different machine learning algorithms. Results revealed that a novel combination of partial directed coherence and multi-layer perceptron (along with recursive feature elimination) resulted in a high classification accuracy of 95.44%. We discuss the implications of our results in developing real-time neurofeedback systems for assessing and enhancing executive functioning performance post-tDCS administration.  \nKeywords—Functional connectivity, transcranial direct current stimulation, magnitude squared coherence, partial directed coherence, wavelet coherence, Electroencephalography, multi-layer perceptron.  \nI. INTRODUCTION  \nExecutive functioning is a set of higher-order cognitive processes necessary for goal-directed activities, decisionmaking, problem-solving, and adaptive functioning in everyday life [1]. It entails various mental processes that assist individuals  \nin organizing, planning, initiating, and monitoring their actions and managing their emotions and attention [1] . The prefrontal cortex (PFC), a region of the brain positioned directly behind the forehead, is the center of executive functioning [1] . The dorsolateral prefrontal cortex (DLPFC) is a significant subregion important in working memory, cognitive flexibility, and attentional regulation. Another important component of the executive functioning network is the anterior cingulate cortex (ACC), which is responsible for conflict resolution, error detection, and decision-making [1] . Moreover, the executive functioning system has an elevated level of interaction with different brain regions engaged in diverse cognitive tasks.  \nIn recent years, there has been growing interest in evaluating the efficacy of different techniques to enhance executive functioning [2] . Among these techniques, non-invasive brain stimulation techniques, like tDCS, have found considerable traction in the past decade. tDCS modifies neuronal activity bypassing weak electrical currents through specific brain areas. tDCS applied across the dorsolateral prefrontal cortex (DLPFC), a critical brain region involved in executive functions, has been found in","cbCaijTL0UMr6fpZ","https://ap.wps.com/l/cbCaijTL0UMr6fpZ","pdf",238213,1,7,"English","en",105,"# Introduction\n## Executive functioning and brain networks\n## Non-invasive stimulation and tDCS\n## EEG and functional connectivity\n## Machine learning for EEG classification","[{\"question\":\"What is the purpose of using functional connectivity and machine learning in this study?\",\"answer\":\"To classify executive functioning performance after longitudinal tDCS by extracting functional connectivity metrics from EEG and feeding them into machine learning algorithms.\"},{\"question\":\"How was the tDCS intervention structured for the experimental and control groups?\",\"answer\":\"Fifty subjects were split into experimental and placebo control groups. The experimental group received tDCS during task training from Day 2 to Day 8, while the control group received sham tDCS, and both groups repeated the task on Day 10.\"},{\"question\":\"Which modeling approach achieved the highest classification accuracy and how high was it?\",\"answer\":\"A combination of partial directed coherence with a multi-layer perceptron, together with recursive feature elimination, produced a classification accuracy of 95.44%.\"}]","Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods | PDF",1785818979,18,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"classification-of-executive-functioning-performance-post-longitudinal-tdcs-using-functional-connectivity-and-machine-learning-methods","",{"@graph":36,"@context":86},[37,54,69],{"@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-executive-functioning-performance-post-longitudinal-tdcs-using-functional-connectivity-and-machine-learning-methods/123867/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the purpose of using functional connectivity and machine learning in this study?","Question",{"text":76,"@type":77},"To classify executive functioning performance after longitudinal tDCS by extracting functional connectivity metrics from EEG and feeding them into machine learning algorithms.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the tDCS intervention structured for the experimental and control groups?",{"text":81,"@type":77},"Fifty subjects were split into experimental and placebo control groups. The experimental group received tDCS during task training from Day 2 to Day 8, while the control group received sham tDCS, and both groups repeated the task on Day 10.",{"name":83,"@type":74,"acceptedAnswer":84},"Which modeling approach achieved the highest classification accuracy and how high was it?",{"text":85,"@type":77},"A combination of partial directed coherence with a multi-layer perceptron, together with recursive feature elimination, produced a classification accuracy of 95.44%.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]