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Classification of Music Preferences Using EEG Data in Machine Learning Models

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dc.contributor.authorVedder, Helen
dc.contributor.authorStano, Fabio
dc.contributor.authorKnierim, Michael
dc.date.accessioned2024-08-21T11:08:29Z
dc.date.available2024-08-21T11:08:29Z
dc.date.issued2024
dc.description.abstractIn this paper, we investigate how EEG data can be used to predict individual music preferences. Our study relies on machine learning and specially developed models such as EEGNet to analyze participants' brain activity while listening to music. Participants listened to music excerpts, rated them, and their EEG data were recorded. We extracted relevant features from the EEG data and used convolutional neural networks (CNNs) to classify music preferences. Our results show that our models are able to predict music preferences with an accuracy of up to 69%. This confirms the potential of EEG in personalized music recommendation and demonstrates the feasibility of integrating EEG into wearable devices to improve the user experience.en
dc.identifier.doi10.18420/muc2024-mci-src-324
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/44234
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2024 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.rightshttp://purl.org/eprint/accessRights/RestrictedAccess
dc.rights.urihttp://purl.org/eprint/accessRights/RestrictedAccess
dc.subjectEEG
dc.subjectBCI
dc.subjectMusic Preference
dc.subjectMachine Learning
dc.subjectClassification
dc.subjectEEGNet
dc.titleClassification of Music Preferences Using EEG Data in Machine Learning Modelsen
dc.typeText/Conference Paper
gi.conference.date1.-4. September 2024
gi.conference.locationKarlsruhe
gi.conference.sessiontitleMCI: Student Research Competition

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