On the efficiency of LSTM in classifying musical impressions from EEG recordings

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dc.contributor.authorKaya, Burak
dc.contributor.authorHabiboğlu, Mehmet Gökhan
dc.contributor.authorMoghaddamnia, Sanam
dc.date.accessioned2024-11-13T19:21:06Z
dc.date.available2024-11-13T19:21:06Z
dc.date.issued2023
dc.departmentTAÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe objective of this study is the classification of musical impressions with long short-term memory (LSTM) approach using EEG recordings of 20 subjects, while listening to different music genres. For this purpose, a deep learning model was developed, where relevant features extracted from intrinsic mode functions (IMF) of the clean EEG data are used as the input signals. The classification accuracy of the proposed model is evaluated with various feature sets. The highest classification accuracy is 73.33%, which is achieved by combining higher-order statistics and the first difference of IMF features.
dc.identifier.citationKaya, B., Habiboğlu, Mehmet G., Moghaddamnia, S. (2022). On the efficiency of LSTM in classifying musical impressions from EEG recordings. 6th International Conference of Mathematical Sciences (ICMS 2022), 2879 (1).
dc.identifier.doi10.1063/12.0023973
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85183305737
dc.identifier.urihttps://pubs.aip.org/aip/acp/article-abstract/2879/1/040014/2928673/On-the-efficiency-of-long-short-term-memory-in?redirectedFrom=PDF
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1400
dc.identifier.volume2879en_US
dc.indekslendigikaynakScopus
dc.language.isoen
dc.relation.ispartof6th International Conference of Mathematical Sciences (ICMS 2022)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.titleOn the efficiency of LSTM in classifying musical impressions from EEG recordings
dc.typeConference Object

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