{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124314"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124314","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Classification of neuromechanical control strategy in a wrist rotation task","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Ziegelman, Liran"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Neuroscience","degree_department":null,"school":null,"contributors":["Hernandez, Manuel","Hsiao-Wecksler, Elizabeth","Sowers, Richard","Koyejo, Oluwasanmi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Aging","Classification","Control Strategy","Eeg","Machine Learning","Motion","Motor","Neuromechanics","Node"],"languages":["en","eng"],"rights":["Copyright 2024 Liran Ziegelman"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124314","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hernandez, Manuel","Hsiao-Wecksler, Elizabeth","Sowers, Richard","Koyejo, Oluwasanmi"]},{"key":"dc:creator","label":"Author","values":["Ziegelman, Liran"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-19"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Neuroscience"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Aging","Classification","Control Strategy","Eeg","Machine Learning","Motion","Motor","Neuromechanics","Node"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Liran Ziegelman"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124314"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Liran Ziegelman, accepted the attached license on 2024-04-17 at 13:22.","The student, Liran Ziegelman, submitted this Dissertation for approval on 2024-04-17 at 13:33.","This Dissertation was approved for publication on 2024-04-19 at 17:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20459 on 2024-09-16 at 00:35:09","Speed-accuracy trade-offs exist in a variety of neural tasks. It is the goal of this work to establish the presence of and predictability of a motor control strategy during a wrist rotation task. Participants were asked to perform a series of continuous and discrete wrist rotations. This motion data was clustered into segments of either speed or range of motion oriented control strategy, controlling for age cohort, continuity of task, and motion type. Age-related changes in motion and cortical data were explored, as were control strategy related changes. Finally, competing neural ordinary differential equation (NODE) and random forest models were fit to explore the ability to classify control strategy using cortical data alone. The clustering method was found to be successful in establishing control strategy. While age-related changes were not prevalent in direct exploration of motion data, older adults are found to have a lower speed with prioritizing speed as compared to young adults doing the same. Control strategy differences were present in the primary motor cortex at the N1, N2, and P3 components, at the supplementary motor area in the P1 component, and in the prefrontal cortex using prefrontal negativity. When using both motor and prefrontal cortical inputs to competing models, models perform with a similar accuracy but the NODE model was able to train and test data at a much faster pace compared to the random forest."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Classification of neuromechanical control strategy in a wrist rotation task"]}]}],"canonical_facts":{"dc:contributor":["Hernandez, Manuel","Hsiao-Wecksler, Elizabeth","Sowers, Richard","Koyejo, Oluwasanmi"],"dc:creator":["Ziegelman, Liran"],"dc:date":["2024-05","2024-04-19"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Liran Ziegelman, accepted the attached license on 2024-04-17 at 13:22.","The student, Liran Ziegelman, submitted this Dissertation for approval on 2024-04-17 at 13:33.","This Dissertation was approved for publication on 2024-04-19 at 17:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20459 on 2024-09-16 at 00:35:09","Speed-accuracy trade-offs exist in a variety of neural tasks. It is the goal of this work to establish the presence of and predictability of a motor control strategy during a wrist rotation task. Participants were asked to perform a series of continuous and discrete wrist rotations. This motion data was clustered into segments of either speed or range of motion oriented control strategy, controlling for age cohort, continuity of task, and motion type. Age-related changes in motion and cortical data were explored, as were control strategy related changes. Finally, competing neural ordinary differential equation (NODE) and random forest models were fit to explore the ability to classify control strategy using cortical data alone. The clustering method was found to be successful in establishing control strategy. While age-related changes were not prevalent in direct exploration of motion data, older adults are found to have a lower speed with prioritizing speed as compared to young adults doing the same. Control strategy differences were present in the primary motor cortex at the N1, N2, and P3 components, at the supplementary motor area in the P1 component, and in the prefrontal cortex using prefrontal negativity. When using both motor and prefrontal cortical inputs to competing models, models perform with a similar accuracy but the NODE model was able to train and test data at a much faster pace compared to the random forest."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124314"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Liran Ziegelman"],"dc:subject":["Aging","Classification","Control Strategy","Eeg","Machine Learning","Motion","Motor","Neuromechanics","Node"],"dc:title":["Classification of neuromechanical control strategy in a wrist rotation task"],"dc:type":["text"],"thesis:degree_discipline":["Neuroscience"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}