EEG-based Emotion Recognition with Music: A Model and Application

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dc.contributor.authorScavotto, Zakariyya
dc.date.accessioned2022-11-21T14:11:09Z
dc.date.available2022-11-21T14:11:09Z
dc.date.issued2022-11
dc.descriptionSenior Research Project, Thomas Jefferson High School for Science and Technology in Collaboration with George Mason University Neural Engineering Lab. November 2022.
dc.description.abstractWith the growth of music streaming, both for pleasure and other applications, such as music therapy, being able to understand how music makes someone feel has increased in importance. The goal of this study was twofold: first, create a machine learning model to predict a subject’s emotional response to music; then integrate this trained model into an application that can predict someone’s emotional response based on live data. Using support vector machines (SVMs) as the basis of the machine learning model, a model was trained to recognize the correct emotional response with 64% accuracy, and the model was successfully implemented into a demonstration web application.
dc.identifier.citationScavotto, Zakariyya. EEG-based Emotion Recognition with Music: A Model and Application. Senior Research Project, Thomas Jefferson High School for Science and Technology in Collaboration with George Mason University Neural Engineering Lab. November 2022.
dc.identifier.urihttps://hdl.handle.net/1920/12993
dc.language.isoen_US
dc.rightsAttribution-NonCommercial 3.0 United States
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/us/
dc.subjectEEG
dc.subjectEmotion Recognition
dc.subjectMachine learning
dc.titleEEG-based Emotion Recognition with Music: A Model and Application
dc.typeWorking Paper

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