{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/34790"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/34790","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Optimizing the Analysis of Electroencephalographic Data by Dynamic Graphs","abstract":"The brain’s underlying functional connectivity has been recently studied using tools offered by graph theory and network theory. Although the primary research focus in this area has so far been mostly on static graphs, the complex and dynamic nature of the brain’s underlying mechanism has initiated the usage of dynamic graphs, providing groundwork for time sensi- tive and finer investigations. Studying the topological reconfiguration of these dynamic graphs is done by exploiting a pool of graph metrics, which describe the network’s characteristics at different scales. However, considering the vast amount of data generated by neuroimaging tools, heavy computation load and limited amount of time and resources, it is vital to refine this pool of metrics to avoid using non-informative and redundant ones. In this study, we use electroencephalographic (EEG) brain signals, taken from recordings in 5 different experimental conditions, to generate the dynamic graphs by moving a sliding win- dow over the time series. Dynamic graphs are produced under various conditions that are a combination of different window sizes, different numbers of shared time points and various frequency bands. Based on each set of these dynamic graphs, time series of 25 graph metrics, and then their pairwise correlation values are computed. This is done to investigate the metric correlations under various circumstances, and to detect the ones that are always present. We conclude by suggesting a set of uniquely informative and orthogonal metrics that is conve- nient to use for further analysis of brain’s functional connectivity","abstract_html":"The brain’s underlying functional connectivity has been recently studied using tools offered by graph theory and network theory. Although the primary research focus in this area has so far been mostly on static graphs, the complex and dynamic nature of the brain’s underlying mechanism has initiated the usage of dynamic graphs, providing groundwork for time sensi- tive and finer investigations. Studying the topological reconfiguration of these dynamic graphs is done by exploiting a pool of graph metrics, which describe the network’s characteristics at different scales. However, considering the vast amount of data generated by neuroimaging tools, heavy computation load and limited amount of time and resources, it is vital to refine this pool of metrics to avoid using non-informative and redundant ones. In this study, we use electroencephalographic (EEG) brain signals, taken from recordings in 5 different experimental conditions, to generate the dynamic graphs by moving a sliding win- dow over the time series. Dynamic graphs are produced under various conditions that are a combination of different window sizes, different numbers of shared time points and various frequency bands. Based on each set of these dynamic graphs, time series of 25 graph metrics, and then their pairwise correlation values are computed. This is done to investigate the metric correlations under various circumstances, and to detect the ones that are always present. We conclude by suggesting a set of uniquely informative and orthogonal metrics that is conve- nient to use for further analysis of brain’s functional connectivity","abstract_has_math":false,"creators":["Golestaneh, Mehrsasadat"],"institution":"The University of Western Ontario","degree_name":"M Sc","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Mark Joseph Daley"],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-04-17","date_published":"2014-04-17","updated_at":"2026-07-27T21:56:11Z","subjects":["Dynamic graphs","Orthogonal metrics","Functional connectivity","EEG"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/34790","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Mark Joseph Daley"]},{"key":"dc:creator","label":"Author","values":["Golestaneh, Mehrsasadat"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T20:33:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T20:33:30Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-04-17"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M Sc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Dynamic graphs","Orthogonal metrics","Functional connectivity","EEG"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_ca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/34790"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["The brain’s underlying functional connectivity has been recently studied using tools offered by graph theory and network theory. Although the primary research focus in this area has so far been mostly on static graphs, the complex and dynamic nature of the brain’s underlying mechanism has initiated the usage of dynamic graphs, providing groundwork for time sensi- tive and finer investigations. Studying the topological reconfiguration of these dynamic graphs is done by exploiting a pool of graph metrics, which describe the network’s characteristics at different scales. However, considering the vast amount of data generated by neuroimaging tools, heavy computation load and limited amount of time and resources, it is vital to refine this pool of metrics to avoid using non-informative and redundant ones. In this study, we use electroencephalographic (EEG) brain signals, taken from recordings in 5 different experimental conditions, to generate the dynamic graphs by moving a sliding win- dow over the time series. Dynamic graphs are produced under various conditions that are a combination of different window sizes, different numbers of shared time points and various frequency bands. Based on each set of these dynamic graphs, time series of 25 graph metrics, and then their pairwise correlation values are computed. This is done to investigate the metric correlations under various circumstances, and to detect the ones that are always present. We conclude by suggesting a set of uniquely informative and orthogonal metrics that is conve- nient to use for further analysis of brain’s functional connectivity"]},{"key":"dc:title","label":"Title","values":["Optimizing the Analysis of Electroencephalographic Data by Dynamic Graphs"]}]}],"canonical_facts":{"dc:contributor.advisor":["Mark Joseph Daley"],"dc:creator":["Golestaneh, Mehrsasadat"],"dc:date.accessioned":["2025-07-10T20:33:30Z"],"dc:date.available":["2025-07-10T20:33:30Z"],"dc:date.issued":["2014-04-17"],"dc:description":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). 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In this study, we use electroencephalographic (EEG) brain signals, taken from recordings in 5 different experimental conditions, to generate the dynamic graphs by moving a sliding win- dow over the time series. Dynamic graphs are produced under various conditions that are a combination of different window sizes, different numbers of shared time points and various frequency bands. Based on each set of these dynamic graphs, time series of 25 graph metrics, and then their pairwise correlation values are computed. This is done to investigate the metric correlations under various circumstances, and to detect the ones that are always present. 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