[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117304-en":3,"doc-seo-117304-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117304,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","DEVELOPMENT OF MACHINE LEARNING-BASED CONNECTIVITY METHODS FOR EEG DATA - Master Thesis in ICT for Internet and Multimedia","This thesis develops a novel STAGIN-EEG framework for analyzing dynamic functional connectivity using EEG recordings. STAGIN-EEG adapts the Spatio-Temporal Attention Graph Isomorphism Network to EEG, leveraging the high temporal resolution of EEG to model time-varying brain network behavior. Unlike static functional connectivity methods, the proposed approach captures dynamic functional connectivity patterns more faithfully. The framework addresses the limited exploration of EEG-based functional connectivity and is used to classify abnormal versus normal EEG, achieving 78.9% accuracy on the test set.","Department of Information Engineering  \nMaster Thesis in ICT for Internet and Multimedia  \nDEVELOPMENT OF MACHINE LEARNING-BASED CONNECTIVITY METHODS FOR EEG DATA  \nMaster Candidate:  \nDiego Zanutti  \nSupervisor:  \nProf. Nicola Laurenti  \nCo-Supervisor:  \nProf. Giulia Cisotto  \nACADEMIC YEAR 2024-2025 27/02/2025  \nContents  \nAbstract 3  \nIntroduction 4  \n1 Background 6  \n1.1 Neuroimaging Modalities ....................... 6  \n1.1.1 EEG Technology ....................... 6  \n1.1.2 fMRI Technology ....................... 7  \n1.1.3 EEG vs fMRI ......................... 10  \n1.2 Functional Connectivity ........................ 10  \n2 State Of The Art of GNNs in Brain Connectivity 14  \n2.1 fMRI-based GNN Models ...................... 15  \n2.2 EEG-based GNN Models ....................... 16  \n2.3 Related Work ............................. 18  \n2.4 Motivation ............................... 19  \n3 Materials and Methods 20  \n3.1 Dataset ................................ 20  \n3.1.1 Overview ........................... 20  \n3.1.2 Preprocessing ......................... 22  \n3.1.3 Features ............................ 23  \n3.2 Architecture ............................. 25  \n3.2.1 Graph Isomorphism Network ................ 25  \n3.2.2 Graph-Attention Readout ................... 28  \n4 Results and Discussion 30  \n4.1 Implementation ............................ 30  \n4.2 Data Visualization ........................... 31  \n4.3 Connectivity Maps .......................... 33  \n4.4 Classification ............................. 35  \n4.5 Discussion ............................... 40  \nConclusions 42  \nFuture Research Directions ......................... 43  \nReferences 53  \nAbstract  \nThis thesis presents a novel approach, the STAGIN-EEG framework, for analyzing brain connectivity using EEG data. STAGIN-EEG adapts the Spatio-Temporal Attention Graph Isomorphism Network (STAGIN) to EEG, exploiting the high temporal resolution of EEG signals to capture dynamic functional connectivity (dFC) patterns. Unlike traditional static functional connectivity methods, the proposed model accounts for the time-varying nature of brain networks, thus providing a more accurate representation of the brain’s functional dynamics. This work addresses a critical gap in the literature, where EEG-based functional connectivity has not been extensively explored, especially in the context of dynamic patterns in brain connectivity. The model is applied to the binary classification of abnormal and normal EEG, and the results show promising performance, demonstrating that STAGINEEG is a robust tool for dynamic brain connectivity analysis, achieving an accuracy of 78.9% on test set. Findings of this study provide a basis for future research, which can expand on this framework by incorporating additional EEG features and further refining the model to improve its performance and generalizability.  \nIntroduction  \nThe brain is governed by complex mechanisms, and understanding its dynamicsand how it supports its underlying processes remains a subject of ongoing debate [1] . Despite significant advances in neuroscience, the precise processes by which the brain forms networks and functions in support of cognition are still not fully understood [2] . A variety of mechanisms are involved, as the brain operates across both temporal and spatial dimensions, coordinating various typologies of activity [3] . Functional connectivity (FC) describes the relationships between brain regions that are not in direct physical connections through white matter tracts but reflectshow the neuronal activity in one area influences the activity in other areas, instead [4] . By examining these patterns, FC allows for the study of the brain’s organization into distributed, dynamic networks that evolve over time and adapt to different cognitive tasks and states [5] .  \nFC is commonly measured using functional Magnetic Resonance Imaging (fMRI), which provides high spatial resolution to assess brain activity with ","cbCaitJWDsJqUFvA","https://ap.wps.com/l/cbCaitJWDsJqUFvA","pdf",10282289,1,54,"English","en",105,"# Contents\n## Abstract\n## Introduction\n### Background\n#### Neuroimaging Modalities\n#### Functional Connectivity\n## State Of The Art of GNNs in Brain Connectivity\n## Materials and Methods\n## Results and Discussion\n## Conclusions\n## Future Research Directions\n## References","[{\"question\":\"What is the main goal of the STAGIN-EEG framework?\",\"answer\":\"To analyze dynamic functional connectivity from EEG data by adapting a spatio-temporal graph model to capture time-varying brain network patterns.\"},{\"question\":\"How does STAGIN-EEG differ from traditional static functional connectivity methods?\",\"answer\":\"It explicitly accounts for the time-varying nature of brain networks, instead of assuming connectivity remains constant over time.\"},{\"question\":\"What classification task does the thesis perform and what accuracy is reported?\",\"answer\":\"It performs binary classification of abnormal versus normal EEG and reports an accuracy of 78.9% on the test set.\"}]",1785675085,136,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"development-of-machine-learning-based-connectivity-methods-for-eeg-data-master-thesis-in-ict-for-internet-and-multimedia","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/development-of-machine-learning-based-connectivity-methods-for-eeg-data-master-thesis-in-ict-for-internet-and-multimedia/117304/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the STAGIN-EEG framework?","Question",{"text":74,"@type":75},"To analyze dynamic functional connectivity from EEG data by adapting a spatio-temporal graph model to capture time-varying brain network patterns.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does STAGIN-EEG differ from traditional static functional connectivity methods?",{"text":79,"@type":75},"It explicitly accounts for the time-varying nature of brain networks, instead of assuming connectivity remains constant over time.",{"name":81,"@type":72,"acceptedAnswer":82},"What classification task does the thesis perform and what accuracy is reported?",{"text":83,"@type":75},"It performs binary classification of abnormal versus normal EEG and reports an accuracy of 78.9% on the test set.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]