[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124336-en":3,"doc-seo-124336-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"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":27,"seo_description":14,"update_tm":28,"read_time":29},124336,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Exploring Quantum Machine Learning-Enhanced Models for EEG Data Classification - Master of Science Thesis","Electroencephalography (EEG) captures brain activity related to executed and imagined movements, yet separating true motor signals from high-dimensional background noise remains challenging. Reliable classifiers are essential for accurately tracking patient progress over time. This work aligns with the Smart NeuroRehab Ecosystem, aiming to both develop accessible rehabilitation strategies and classify movement-related EEG signals using machine learning.","©Copyright 2025 Stephanie Anne Murray  \nExploring Quantum Machine Learning-Enhanced Models for EEG Data Classification  \nStephanie Anne Murray  \nA thesis submitted in partial fulfillment of the requirements for the degree of  \nMaster of Science  \nUniversity of Washington  \n2025  \nCommittee:  \nErika Parsons  \nPierre Mourad  \nMichael Stiber  \nWooyoung Kim  \nProgram Authorized to Offer Degree: Computer Science & Software Engineering  \nUniversity of Washington  \nAbstract  \nExploring Quantum Machine Learning-Enhanced Models  \nfor EEG Data Classification  \nStephanie Anne Murray  \nChair of the Supervisory Committee:  \nErika Parsons  \nDepartment of Computer Science & SE  \nElectroencephalography (EEG) records brain activity linked to both executed and imagined movements, but separating true motor signals from background noise in high-dimensional EEG data remains a challenge. Reliable classifiers are therefore vital for accurately tracking patient progress over time. This work is part of a larger initiative, the Smart NeuroRehab Ecosystem, which has two primary goals: (1) to propose innovative physical-rehabilitation strategies for neurologic conditions such as stroke using emerging technologies that make therapy more accessible, and (2) to collect and analyze EEG data using machine learning (ML) models that classify movement-related brain signals.  \nEEG data are complex and often difficult to interpret. In this research, we explore the use of quantum machine learning as an alternative approach for EEG signal classification. Compared to classical ML strategies, quantum methods may offer a fundamentally different way of representing and processing data, potentially improving classification performance or computational efficiency. We implement and analyze a ten-qubit Variational Quantum Classifier (VQC), and compare its performance to a tuned Random Forest baseline using EEG data from a publicly available 64-channel dataset. The task involves classifying each EEG time-window as either a movement or rest condition.  \nAcross 40 preliminary runs, the VQC achieves a macro-F 1 score of approximately 0 .75, accuracy of 0.76, and AUROC of 0.83, outperforming the Random Forest (macro-F 1 ≈  \n0.71, AUROC ≈ 0.79) . In addition to higher macro-F 1 and AUROC scores, the VQC also demonstrated significantly better precision and recall on the movement class, based on paired statistical tests. Most experiments were conducted on a quantum simulator, with a subset tested on a cloud-based quantum processor.  \nThese findings suggest that hybrid quantum-classical models can match or exceed the performance of tuned classical pipelines without increasing computational complexity. Within the scope of the Smart NeuroRehab project, this work demonstrates that quantum approaches may offer a practical path to continuous monitoring of EEG in clinical settings. Future improvements in quantum hardware may expand the range of practical applications in biomedical signal analysis.  \nTABLE OF CONTENTS  \nPage  \nList of Figures ....................................... iii  \nList of Tables ........................................ v  \n[Glossary ........................................... vi](Glossary ........................................... vi)  \n[Chapter 1: Introduction ................................ 1](Chapter 1: Introduction ................................ 1)  \n[1.1 Problem Statement ................................ 3](1.1 Problem Statement ................................ 3)  \n[1.2 Quantum Machine Learning as a Possible Solution ............... 4](1.2 Quantum Machine Learning as a Possible Solution ............... 4)  \n[1.3 Research Questions ................................ 4](1.3 Research Questions ................................ 4)  \n[1.4 Method Overview ................................. 6](1.4 Method Overview ................................. 6)  \n[1.5 Stakeholders and Expected Benefits ....................... 7](1.5 Stakeholders and Expected Benefits ...........","cbCait5zDNiCDXyj","https://ap.wps.com/l/cbCait5zDNiCDXyj","pdf",9250967,1,103,"English","en",105,"# Abstract\n# Chapter 1: Introduction\n## 1.1 Problem Statement\n## 1.2 Quantum Machine Learning as a Possible Solution\n## 1.3 Research Questions\n## 1.4 Method Overview\n## 1.5 Stakeholders and Expected Benefits\n## 1.6 Thesis Outline\n# Chapter 2: Related Work\n## 2.1 Literature Review\n## 2.2 Existing Approaches\n# Chapter 3: Technical Background\n## 3.1 Electroencephalography for Motor Tasks\n## 3.2 Classical Machine-Learning Pipelines for EEG\n## 3.3 Quantum-Computing Foundations\n## 3.4 Variational Quantum Classifiers (Circuits)\n## 3.5 Why Quantum for EEG Classification\n# Chapter 4: Methods\n## 4.1 Dataset\n## 4.2 Pre-processing\n## 4.3 Variational Quantum Classifier\n## 4.4 Evaluation\n## 4.5 Summary of Experiments\n# Chapter 5: Results\n## 5.1 VQC Architecture and Optimization Effects\n## 5.2 Optimizer Comparison\n## 5.3 Ansatz configuration\n## 5.4 Simulation vs. Real Hardware: Time Complexity Perspective\n## 5.5 Final Results\n# Chapter 6: Conclusion","[{\"question\":\"What problem does the thesis address in EEG-based movement analysis?\",\"answer\":\"It addresses the difficulty of distinguishing true motor signals from background noise in high-dimensional EEG data, which complicates reliable classification of movement-related activity.\"},{\"question\":\"How does the research evaluate the quantum approach?\",\"answer\":\"It implements a ten-qubit Variational Quantum Classifier (VQC) and compares its results to a tuned Random Forest baseline using EEG from a publicly available 64-channel dataset.\"},{\"question\":\"What performance results does the VQC achieve and how does it compare to the baseline?\",\"answer\":\"Across 40 preliminary runs, the VQC reaches about 0.75 macro-F1, 0.76 accuracy, and 0.83 AUROC, outperforming Random Forest (macro-F1 ≈ 0.71, AUROC ≈ 0.79) with improved precision and recall for the movement class.\"}]","Exploring Quantum Machine Learning-Enhanced Models for EEG Data Classification - Master of Science Thesis | PDF",1785821667,260,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"exploring-quantum-machine-learning-enhanced-models-for-eeg-data-classification-master-of-science-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/exploring-quantum-machine-learning-enhanced-models-for-eeg-data-classification-master-of-science-thesis/124336/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address in EEG-based movement analysis?","Question",{"text":75,"@type":76},"It addresses the difficulty of distinguishing true motor signals from background noise in high-dimensional EEG data, which complicates reliable classification of movement-related activity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the research evaluate the quantum approach?",{"text":80,"@type":76},"It implements a ten-qubit Variational Quantum Classifier (VQC) and compares its results to a tuned Random Forest baseline using EEG from a publicly available 64-channel dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance results does the VQC achieve and how does it compare to the baseline?",{"text":84,"@type":76},"Across 40 preliminary runs, the VQC reaches about 0.75 macro-F1, 0.76 accuracy, and 0.83 AUROC, outperforming Random Forest (macro-F1 ≈ 0.71, AUROC ≈ 0.79) with improved precision and recall for the movement class.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]