{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120453"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120453","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Extracting interpretable features from large scale clinical EEGs using unsupervised learning","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Gupta, Teja Borra"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Varatharajah, Yogatheesan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Eeg","Neurological Disorders, Tensor Decomposition","Unsupervised Learning","Interpretability","Variational Autoencoder"],"languages":["en","eng"],"rights":["Copyright 2023 Teja Gupta"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120453","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Varatharajah, Yogatheesan"]},{"key":"dc:creator","label":"Author","values":["Gupta, Teja Borra"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-05-03"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Eeg","Neurological Disorders, Tensor Decomposition","Unsupervised Learning","Interpretability","Variational Autoencoder"]}]},{"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 2023 Teja Gupta"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120453"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","The student, Teja Gupta, accepted the attached license on 2023-05-02 at 13:39.","The student, Teja Gupta, submitted this Thesis for approval on 2023-05-02 at 15:25.","This Thesis was approved for publication on 2023-05-03 at 15:40.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19299 on 2023-09-01 at 17:15:53","Analyzing clinical electroencephalograms (EEG) is crucial for diagnosing and monitoring neurological disorders. However, manual expert review is not scalable and is prone to errors. Thus, more efficient and reliable methods are needed. Current methods rely on two-dimensional decompositions such as principal component analysis (PCA) or indepedent component anal ysis (ICA) and deep learning methods such as autoencoders (AE) and self-supervised learning (SSL). However, these methods do not retain the naturalstructure of the data and not easily interpretable. To overcome these limitations, we propose using tensor decomposition (TD) to extract interpretable and clinically useful features from EEGs. Tensor decomposition retains the natural structure of the data and provides a more efficient and reliable alternative to traditional approaches. Additionally, to address the lack of expressivity with tensor decomposition, we explore ways to incorporate tensor decomposition with the variational autoencoder framework."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Extracting interpretable features from large scale clinical EEGs using unsupervised learning"]}]}],"canonical_facts":{"dc:contributor":["Varatharajah, Yogatheesan"],"dc:creator":["Gupta, Teja Borra"],"dc:date":["2023-05","2023-05-03"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","The student, Teja Gupta, accepted the attached license on 2023-05-02 at 13:39.","The student, Teja Gupta, submitted this Thesis for approval on 2023-05-02 at 15:25.","This Thesis was approved for publication on 2023-05-03 at 15:40.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19299 on 2023-09-01 at 17:15:53","Analyzing clinical electroencephalograms (EEG) is crucial for diagnosing and monitoring neurological disorders. However, manual expert review is not scalable and is prone to errors. Thus, more efficient and reliable methods are needed. Current methods rely on two-dimensional decompositions such as principal component analysis (PCA) or indepedent component anal ysis (ICA) and deep learning methods such as autoencoders (AE) and self-supervised learning (SSL). However, these methods do not retain the naturalstructure of the data and not easily interpretable. To overcome these limitations, we propose using tensor decomposition (TD) to extract interpretable and clinically useful features from EEGs. Tensor decomposition retains the natural structure of the data and provides a more efficient and reliable alternative to traditional approaches. Additionally, to address the lack of expressivity with tensor decomposition, we explore ways to incorporate tensor decomposition with the variational autoencoder framework."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120453"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Teja Gupta"],"dc:subject":["Eeg","Neurological Disorders, Tensor Decomposition","Unsupervised Learning","Interpretability","Variational Autoencoder"],"dc:title":["Extracting interpretable features from large scale clinical EEGs using unsupervised learning"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}