{"id":{"repo_id":"uic","oai_identifier":"oai:figshare.com:article/32995136"},"canonical_url":"https://search.dev.ndltd.org/etd/uic/oai:figshare.com:article/32995136","repository":{"repo_id":"uic","name":"University of Illinois - Chicago","base_url":"https://api.figshare.com/v2/oai"},"display":{"title":"New Approaches in Nonlinear Analysis and Modeling of Multichannel Signals","abstract":"In this thesis, we present novel machine-learning-driven approaches for nonlinear analysis and modeling of complex multichannel signals, focusing on two key applications: Electroencephalography (EEG) classification and analysis, and phase retrieval-based image reconstruction. EEG serves as an effective diagnostic tool for mental disorders and neurological abnormalities and improving its analysis can enhance classification performance. We propose a novel EEG data representation that leverages the spatial layout of sensors and preserves their topology to improve classification accuracy and computing cost of EEG analysis. Compared to traditional one-dimensional channel concatenation, our model consistently boosts accuracy by 5–8% across multiple machine learning algorithms in different EEG-based problems. The phase retrieval problem is an inverse problem which consists of recovering a constrained image from the magnitude of its Fourier transform. It’s fundamental in a variety of fields and imaging systems such as Crystallography, X-ray, optical imaging, and astronomical imaging. Although traditional algorithms such as the hybrid input-output (HIO) method, and continuous hybrid input–output (CHIO) are widely used to solve this problem, their reconstruction performance can be improved. We introduce a new hybrid model that consists of CHIO methods and deep neural networks to solve this inverse problem. The new model achieves better reconstruction performance with minimal additional computational cost.","abstract_html":"In this thesis, we present novel machine-learning-driven approaches for nonlinear analysis and modeling of complex multichannel signals, focusing on two key applications: Electroencephalography (EEG) classification and analysis, and phase retrieval-based image reconstruction. EEG serves as an effective diagnostic tool for mental disorders and neurological abnormalities and improving its analysis can enhance classification performance. We propose a novel EEG data representation that leverages the spatial layout of sensors and preserves their topology to improve classification accuracy and computing cost of EEG analysis. Compared to traditional one-dimensional channel concatenation, our model consistently boosts accuracy by 5–8% across multiple machine learning algorithms in different EEG-based problems. The phase retrieval problem is an inverse problem which consists of recovering a constrained image from the magnitude of its Fourier transform. It’s fundamental in a variety of fields and imaging systems such as Crystallography, X-ray, optical imaging, and astronomical imaging. Although traditional algorithms such as the hybrid input-output (HIO) method, and continuous hybrid input–output (CHIO) are widely used to solve this problem, their reconstruction performance can be improved. We introduce a new hybrid model that consists of CHIO methods and deep neural networks to solve this inverse problem. The new model achieves better reconstruction performance with minimal additional computational cost.","abstract_has_math":false,"creators":["Lubna Shibly Mokatren (24400091)"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05-01T00:00:00Z","date_published":"2026-05-01T00:00:00Z","updated_at":"2026-07-27T21:33:48Z","subjects":["EEG Analysis with Spatial Correlation Using Deep Learning","Phase Retrieval"],"languages":[],"rights":["In Copyright","Open Access after 2028-05-01"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.25417/uic.32995136.v1","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Lubna Shibly Mokatren (24400091)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-05-01T00:00:00Z"]},{"key":"dc:relation","label":"Dc Relation","values":["https://figshare.com/articles/thesis/New_Approaches_in_Nonlinear_Analysis_and_Modeling_of_Multichannel_Signals/32995136"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["EEG Analysis with Spatial Correlation Using Deep Learning","Phase Retrieval"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright","Open Access after 2028-05-01"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10.25417/uic.32995136.v1"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this thesis, we present novel machine-learning-driven approaches for nonlinear analysis and modeling of complex multichannel signals, focusing on two key applications: Electroencephalography (EEG) classification and analysis, and phase retrieval-based image reconstruction. EEG serves as an effective diagnostic tool for mental disorders and neurological abnormalities and improving its analysis can enhance classification performance. We propose a novel EEG data representation that leverages the spatial layout of sensors and preserves their topology to improve classification accuracy and computing cost of EEG analysis. Compared to traditional one-dimensional channel concatenation, our model consistently boosts accuracy by 5–8% across multiple machine learning algorithms in different EEG-based problems. The phase retrieval problem is an inverse problem which consists of recovering a constrained image from the magnitude of its Fourier transform. It’s fundamental in a variety of fields and imaging systems such as Crystallography, X-ray, optical imaging, and astronomical imaging. Although traditional algorithms such as the hybrid input-output (HIO) method, and continuous hybrid input–output (CHIO) are widely used to solve this problem, their reconstruction performance can be improved. We introduce a new hybrid model that consists of CHIO methods and deep neural networks to solve this inverse problem. The new model achieves better reconstruction performance with minimal additional computational cost."]},{"key":"dc:title","label":"Title","values":["New Approaches in Nonlinear Analysis and Modeling of Multichannel Signals"]}]}],"canonical_facts":{"dc:creator":["Lubna Shibly Mokatren (24400091)"],"dc:date":["2026-05-01T00:00:00Z"],"dc:description":["In this thesis, we present novel machine-learning-driven approaches for nonlinear analysis and modeling of complex multichannel signals, focusing on two key applications: Electroencephalography (EEG) classification and analysis, and phase retrieval-based image reconstruction. EEG serves as an effective diagnostic tool for mental disorders and neurological abnormalities and improving its analysis can enhance classification performance. We propose a novel EEG data representation that leverages the spatial layout of sensors and preserves their topology to improve classification accuracy and computing cost of EEG analysis. Compared to traditional one-dimensional channel concatenation, our model consistently boosts accuracy by 5–8% across multiple machine learning algorithms in different EEG-based problems. The phase retrieval problem is an inverse problem which consists of recovering a constrained image from the magnitude of its Fourier transform. It’s fundamental in a variety of fields and imaging systems such as Crystallography, X-ray, optical imaging, and astronomical imaging. Although traditional algorithms such as the hybrid input-output (HIO) method, and continuous hybrid input–output (CHIO) are widely used to solve this problem, their reconstruction performance can be improved. We introduce a new hybrid model that consists of CHIO methods and deep neural networks to solve this inverse problem. The new model achieves better reconstruction performance with minimal additional computational cost."],"dc:identifier":["10.25417/uic.32995136.v1"],"dc:relation":["https://figshare.com/articles/thesis/New_Approaches_in_Nonlinear_Analysis_and_Modeling_of_Multichannel_Signals/32995136"],"dc:rights":["In Copyright","Open Access after 2028-05-01"],"dc:subject":["EEG Analysis with Spatial Correlation Using Deep Learning","Phase Retrieval"],"dc:title":["New Approaches in Nonlinear Analysis and Modeling of Multichannel Signals"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T21:33:48Z"}