Kumagai, Y., Arvaneh, M., Okawa, H. et al. (2 more authors) (2017) Classification of Familiarity Based on Cross-Correlation Features Between EEG and Music. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 11-15 Jul 2017, Seogwipo, South Korea. IEEE , pp. 2879-2882.
Abstract
An approach to recognize the familiarity of a listener with music using both the electroencephalogram (EEG) signals and the music signal is proposed in this paper. Eight participants listened to melodies produced by piano sounds as simple natural stimuli. We classified the familiarity of each participant using cross-correlation values between EEG and the envelope of the music signal as features of the support vector machine (SVM) or neural network used. Here, we report that the maximum classification accuracy was 100% obtained by the SVM. These results suggest that the familiarity of music can be classified by cross-correlation values. The proposed approach can be used to recognize high-level brain states such as familiarity, preference, and emotion.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. This is an author produced version of a paper subsequently published in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number SMALL GRANT DAIWA ANGLO JAPANESE FOUNDATION |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 May 2017 11:06 |
Last Modified: | 19 Dec 2022 13:36 |
Published Version: | https://doi.org/10.1109/EMBC.2017.8037458 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/EMBC.2017.8037458 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:116452 |