[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125757-en":3,"doc-seo-125757-105":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},125757,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Use of EEG-Based Machine Learning to Predict Music-Related Brain Activity - Undergraduate Thesis","Music has strong emotional and cognitive effects and is widely regarded as capable of influencing the brain physically and emotionally. Building on clinically relevant music therapy and its neuroplastic mechanisms, this thesis investigates whether musical thoughts can be decoded using machine learning from EEG recordings. EEG data were collected while participants focused on five melodies, and models were trained on labeled datasets to predict which melody subjects were thinking of. Prediction accuracy ranged from 45% to 80%, suggesting potential for examining neuroplasticity and supporting future music-based brain-computer interfaces.","University of Lynchburg  \nDigital Showcase @ University of Lynchburg  \n\n| Undergraduate Theses and Capstone Projects | Student Publications |\n| --- | --- |\n| Spring 5-1-2023\u003Cbr>Use of EEG-Based Machine Learning to Predict Music-Related Brain Activity\u003Cbr>Charles Skutt\u003Cbr>University of Lynchburg, [skuttc389@lynchburg.edu](skuttc389@lynchburg.edu)\u003Cbr>Follow this and additional works at: [https://digitalshowcase.lynchburg.edu/utcp](https://digitalshowcase.lynchburg.edu/utcp)\u003Cbr> Part of the Music Therapy Commons, and the Psychology Commons |  |\n\nRecommended Citation  \nSkutt, Charles, \"Use of EEG-Based Machine Learning to Predict Music-Related Brain Activity\" (2023) . Undergraduate Theses and Capstone Projects. 266.  \n[https://digitalshowcase.lynchburg.edu/utcp/266](https://digitalshowcase.lynchburg.edu/utcp/266)  \nThis Thesis is brought to you for free and open access by the Student Publications at Digital Showcase @ University of Lynchburg. It has been accepted for inclusion in Undergraduate Theses and Capstone Projects by an authorized administrator of Digital Showcase @ University of Lynchburg. For more information, please contact [digitalshowcase@lynchburg.edu](digitalshowcase@lynchburg.edu).  \nUse ofEEG-Based Machine Learning to Predict Music-Related Brain Activity  \nCharles Skutt  \nSenior Honors Project  \nSubmitted in partial fulfillment of the graduation requirements of the Westover Honors College  \nWestover Honors College  \nMay, 2023  \n\n| David O. Freier, PhD |\n| --- |\n| Price Blair, PhD |\n\nCynthia Ramsey, DMA  \nAbstract  \nIntroduction  \n1 Music Therapy  \n1.1 Music Therapy and Mental Disorders  \n1.2 Music Therapy and Neurorehabilitation  \n2 Brain Organization-From Neurons to Lobes  \n3 Neuroplasticity  \n3.1 Neuroplasticity-Neurons and Networks  \n3.2 Neuroplasticity-Neurotransmitters  \n3.3 Neuroplasticity-Larger Impacts  \n3.4 Neuroplasticity and Music-Overview  \n3.5 Neuroplasticity and Music-Neurotransmitters  \n3.7 Neuroplasticity-Application  \n4-So, Why Study This Topic? Conclusions From Part 1  \nMethods  \nBrain Visualization Techniques  \nEEG Device Selection  \nEEG Device Options  \nMachine Learning for EEG Data  \nOverview of Machine Learning  \nApplied Use of Machine Learning-XGBoost Classification  \nApplied Use of Machine Learning-Microsoft Azure  \nEEG Data and Rhythms-Brainwaves  \nExperimental Design:  \nOverview  \nSetup  \nData Collection Methods  \nData Organization Methods  \nData Analysis Methods  \nResults  \nDiscussion  \nLimitations  \nConclusion  \nAbstract  \nMusic has many awe-inspiring characteristics. Some may refer to it as a “universal language” with the ability to transcend the barriers of speech, while others may describe its ability to evoke intense emotional experiences for the listener. Regardless of the description, it is a commonly held view that music can have many profound effects. Studies of music’s effects have found these beliefs to be more than pure conjecture, finding that music interacts with and changes our brains in physical and emotional ways.  \nMusic can even have clinical applications, such as music therapy. This type of therapy has been shown to be beneficial in many areas, ranging from stroke rehabilitation to mental health treatment. The mechanisms behind music’s therapeutic benefit has to do with neuroplastic effects; Being able to harness this benefit in a therapeutic setting could make treatments for mental disorders and brain injuries even more effective.  \nThis thesis aimed to discover whether musical thoughts could be interpreted using machine learning, potentially opening the door to the use of thought-based musical training for therapeutic benefit. For this study, EEG data was collected while people were thinking of 5 melodies, then machine learning models were trained on labeled datasets. The models were then tasked with categorizing unlabeled sets ofEEG data-in other words, predicting which melody a subject was thinking of while the data was being recorded. The accuracy of the predictio","cbCaicROGDA13Kou","https://ap.wps.com/l/cbCaicROGDA13Kou","pdf",962307,1,52,"English","en",105,"# Abstract\n# Introduction\n## Music Therapy\n## Brain Organization-From Neurons to Lobes\n## Neuroplasticity\n## Why Study This Topic? Conclusions From Part 1\n# Methods\n## Brain Visualization Techniques\n## EEG Device Selection and Options\n## Machine Learning for EEG Data\n## Applied Use of Machine Learning-XGBoost Classification\n## Applied Use of Machine Learning-Microsoft Azure\n## EEG Data and Rhythms-Brainwaves\n## Experimental Design\n### Overview, Setup, Data Collection and Organization\n### Data Analysis Methods\n# Results\n# Discussion\n# Limitations\n# Conclusion","[{\"question\":\"What is the main goal of this thesis?\",\"answer\":\"To determine whether music-related thoughts can be interpreted from EEG data using machine learning, enabling prediction of which melody a subject is thinking about during recording.\"},{\"question\":\"How was the EEG data used in the machine learning workflow?\",\"answer\":\"EEG data were collected while participants focused on five melodies, then machine learning models were trained on labeled datasets and used to classify unlabeled EEG segments.\"},{\"question\":\"What level of prediction accuracy did the models achieve?\",\"answer\":\"Model accuracy ranged from about 45% to 80%, corresponding to roughly 2–4 times better performance than random guessing.\"}]","Use of EEG-Based Machine Learning to Predict Music-Related Brain Activity - Undergraduate Thesis | PDF",1785901043,131,{"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},"use-of-eeg-based-machine-learning-to-predict-music-related-brain-activity-undergraduate-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/use-of-eeg-based-machine-learning-to-predict-music-related-brain-activity-undergraduate-thesis/125757/",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","2026-08-05",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 is the main goal of this thesis?","Question",{"text":75,"@type":76},"To determine whether music-related thoughts can be interpreted from EEG data using machine learning, enabling prediction of which melody a subject is thinking about during recording.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the EEG data used in the machine learning workflow?",{"text":80,"@type":76},"EEG data were collected while participants focused on five melodies, then machine learning models were trained on labeled datasets and used to classify unlabeled EEG segments.",{"name":82,"@type":73,"acceptedAnswer":83},"What level of prediction accuracy did the models achieve?",{"text":84,"@type":76},"Model accuracy ranged from about 45% to 80%, corresponding to roughly 2–4 times better performance than random guessing.","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"]