Classification of Music Preferences Using EEG Data in Machine Learning Models
| dc.contributor.author | Vedder, Helen | |
| dc.contributor.author | Stano, Fabio | |
| dc.contributor.author | Knierim, Michael | |
| dc.date.accessioned | 2024-08-21T11:08:29Z | |
| dc.date.available | 2024-08-21T11:08:29Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | In 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.doi | 10.18420/muc2024-mci-src-324 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/44234 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Mensch und Computer 2024 - Workshopband | |
| dc.relation.ispartofseries | Mensch und Computer | |
| dc.rights | http://purl.org/eprint/accessRights/RestrictedAccess | |
| dc.rights.uri | http://purl.org/eprint/accessRights/RestrictedAccess | |
| dc.subject | EEG | |
| dc.subject | BCI | |
| dc.subject | Music Preference | |
| dc.subject | Machine Learning | |
| dc.subject | Classification | |
| dc.subject | EEGNet | |
| dc.title | Classification of Music Preferences Using EEG Data in Machine Learning Models | en |
| dc.type | Text/Conference Paper | |
| gi.conference.date | 1.-4. September 2024 | |
| gi.conference.location | Karlsruhe | |
| gi.conference.sessiontitle | MCI: Student Research Competition |
Dateien
Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
- Name:
- muc2024-mci-src-324.pdf
- Größe:
- 1023.04 KB
- Format:
- Adobe Portable Document Format