[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120115-en":3,"doc-seo-120115-105":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":4,"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},120115,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Detection of Dementia: Using Electroencephalography and Machine Learning - Master’s Thesis","Dementia describes a decline in mental ability that disrupts daily life, and this thesis evaluates EEG (electroencephalography) signals as a non-invasive way to support dementia detection during language tasks. The work implements multiple EEG feature extraction and selection techniques, then applies clustering and supervised machine learning models to identify informative patterns. Results summarize demographic tendencies and show that clustering reached a top K-means Silhouette Score of about 0.295, while Decision Tree and Random Forest achieved the highest accuracy at 95.83%.","Detection of Dementia: Using Electroencephalography and Machine Learning  \nby  \nTanveer Ahmed  \nM.Eng., NED University, 2020  \nA Thesis Submitted in Partial Fulfillment of the Requirements  \nfor the Degree of  \nMASTER OF APPLIED SCIENCE  \nin the Department of Electrical and Computer Engineering  \n© Tanveer Ahmed, 2023 University of Victoria  \nAll rights reserved. This thesis may not be reproduced in whole or in part, by photocopying or other means, without the permission of the author.  \nDetection of Dementia: Using Electroencephalography and Machine Learning  \nby  \nTanveer Ahmed  \nM.Eng., NED University, 2020  \nSupervisory Committee  \nDr. Fayez Gebali, Co-Supervisor  \n(Department of Electrical and Computer Engineering)  \nDr. Haytham El Miligi, Co-Supervisor  \n(Department of Electrical and Computer Engineering)  \nAbstract  \nDementia is a general term used to describe a decline in mental ability that interferes with daily life. This thesis aims to investigate the use ofEEG (Electroencephalography) signals to detect dementia, which offers a promising approach in individuals with dementia, as they provide anon-invasive measure of brain activity during language tasks, which can be analyzed using machine learning algorithms to identify patterns. We also implemented various EEG features extraction and selection techniques and machine learning algorithms that have been used and provide an analysis of the results obtained. We also reported that the most people in the age bracket of 60-69 are most likely to have dementia, with females in common. Overall, K-means achieved the highest Silhouette Score for our clustering results is approximately 0.295. And Decision Tree and Random Forest models achieved the best accuracy of 95.83% . The SVMand Logistic Regression models also achieved good accuracy of 91.67% with the Decision Tree and Random Forest slightly outperforming them.  \nTable of Contents  \nSupervisory Committee................................................................................................................. ii  \nAbstract ......................................................................................................................................... iii  \nTable of Contents ...........................................................................................................................iv  \n[List of Tables ..................................................................................................................................vi](List of Tables ..................................................................................................................................vi)  \nList of Figures .............................................................................................................................. vii  \nList of Abrevations ..................................................................................................................... viii  \nAcknowledgements ........................................................................................................................ix  \nCHAPTER 1 INTODUCTION 1  \n1.1 PROBLEM STATEMENT .................................................................................................... 1  \n1.2 MOTIVATION ...................................................................................................................... 3  \n1.3 RESEARCH GOALS............................................................................................................. 4  \n1.4 RESEARCH CONTRIBUTIONS.......................................................................................... 4  \n1.5 THESIS ORGANIZATION ................................................................................................... 4  \nCHAPTER 2 BACKGROUND AND RELATED WORK 5  \n2. 1 BACKGROUND.................................................................................................................... 5  \n2.2 RELATED WORK ......................................","cbCaihXv9jpYyFwU","https://ap.wps.com/l/cbCaihXv9jpYyFwU","pdf",2780179,1,58,"English","en",105,"# Supervisory Committee\n# Abstract\n# Table of Contents\n# Chapter 1 Introduction\n## 1.1 Problem Statement\n## 1.2 Motivation\n## 1.3 Research Goals\n## 1.4 Research Contributions\n## 1.5 Thesis Organization\n# Chapter 2 Background and Related Work\n## 2.1 Background\n## 2.2 Related Work\n## 2.3 EEG Signal Processing and Machine Learning\n## 2.4 Comparison of Previously Used Dataset and Algorithms\n## 2.5 Summary and Conclusion of Previous Research Findings\n# Chapter 3 Proposed Methodology\n## 3.1 Data Collection\n## 3.2 Data Preprocessing\n## 3.3 Feature Engineering\n## 3.4 Feature Extraction\n## 3.5 Feature Selection\n## 3.6 Machine Learning\n## 3.7 Model Evaluation\n# Chapter 4 Results and Discussions\n## 4.1 Clustering\n## 4.2 Time-based Segmentation\n## 4.2.1 Synthetic Labels Creation\n## 4.3 Comparison of Different Demographic Groups\n## 4.4 Comparison of Results with Other Modalities\n## 4.5 Discussion","[{\"question\":\"How does the thesis use EEG to detect dementia?\",\"answer\":\"It uses EEG signals recorded during language tasks as a non-invasive measure of brain activity, then analyzes the signals by applying feature extraction/selection and machine learning to identify distinguishing patterns.\"},{\"question\":\"Which machine learning models achieved the best accuracy?\",\"answer\":\"Decision Tree and Random Forest achieved the best accuracy at 95.83%, while SVM and Logistic Regression also produced strong accuracy around 91.67%.\"},{\"question\":\"What clustering performance did the thesis report?\",\"answer\":\"For clustering, K-means achieved the highest Silhouette Score, approximately 0.295.\"}]","Detection of Dementia: Using Electroencephalography and Machine Learning - Master’s Thesis | PDF",1785728286,146,{"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},"detection-of-dementia-using-electroencephalography-and-machine-learning-masters-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/detection-of-dementia-using-electroencephalography-and-machine-learning-masters-thesis/120115/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the thesis use EEG to detect dementia?","Question",{"text":75,"@type":76},"It uses EEG signals recorded during language tasks as a non-invasive measure of brain activity, then analyzes the signals by applying feature extraction/selection and machine learning to identify distinguishing patterns.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models achieved the best accuracy?",{"text":80,"@type":76},"Decision Tree and Random Forest achieved the best accuracy at 95.83%, while SVM and Logistic Regression also produced strong accuracy around 91.67%.",{"name":82,"@type":73,"acceptedAnswer":83},"What clustering performance did the thesis report?",{"text":84,"@type":76},"For clustering, K-means achieved the highest Silhouette Score, approximately 0.295.","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"]