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EEG Database for musical genres detection.

dc.contributorATIC Research Group, Universidad de Málagaes_ES
dc.contributor.authorAriza Cervera, Isaac
dc.contributor.authorBarbancho-Pérez, Ana María 
dc.contributor.authorTardón-García, Lorenzo José 
dc.contributor.authorBarbancho-Pérez, Isabel 
dc.date.accessioned2025-01-24T13:08:17Z
dc.date.available2025-01-24T13:08:17Z
dc.date.created2024
dc.date.issued2025-01-24
dc.identifier.urihttps://hdl.handle.net/10630/36947
dc.description.abstractThis database is made up of EEG signals from 6 subjects listening to fragments of songs from different musical genres and their answers to the questions: did you know this song and do you like this song?. The musical genres chosen are: ballad, classic, metal and reggaeton. These signals have been captured with the BrainVision actiCHAMP-PLUS system and consist of a total of 64 EEG channels. The BrainVision Recorder software was used to store the signals. The stimulus presentation software used to design the experiment is Eprime 3. For more detailed information on this database, the capture system used and its applications, see [1]. If these data are used for any publication, the following paper must be cited: [1] Isaac Ariza, Ana M. Barbancho, Lorenzo J. Tardón, Isabel Barbancho, Energy-based features and bi-LSTM neural network for EEG-based music and voice classification. Neural Comput & Applic 36, 791–802 (2024). https://doi.org/10.1007/s00521-023-09061-3es_ES
dc.description.sponsorshipFunding for open access publishing: Universidad Málaga/CBUA. This publication is part of Project PID2021-123207NB-I00, funded by MCIN/AEI/10.13039/501100011033/FEDER, UE. This work was partially funded by Junta de Andalucía, Proyectos de I+D+i, in the framework of Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020), under Project No. PY20_00237. Funding for open access charge: Universidad de Málaga/CBUA. This work was done at Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech.es_ES
dc.language.isoenges_ES
dc.publisherUniversidad de Málagaes_ES
dc.relation.isreferencedbyIsaac Ariza, Ana M. Barbancho, Lorenzo J. Tardón, Isabel Barbancho, Energy-based features and bi-LSTM neural network for EEG-based music and voice classification. Neural Comput & Applic 36, 791–802 (2024). https://doi.org/10.1007/s00521-023-09061-3es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectElectroencefalografíaes_ES
dc.subjectEstimulación cerebrales_ES
dc.subjectFormas musicaleses_ES
dc.subject.otherElectroencephalogram (EEG)es_ES
dc.subject.otherMusical genres detectiones_ES
dc.subject.otherEEG classificationes_ES
dc.subject.otherEEG signalses_ES
dc.subject.otherBrain reaction to different auditori stimuluses_ES
dc.titleEEG Database for musical genres detection.es_ES
dc.title.alternativeBase de datos EEG para detección de géneros musicales.es_ES
dc.typeinfo:eu-repo/semantics/datasetes_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.identifier.doi10.24310/riuma.36947
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.publication.year2025
dc.version1.0es_ES
dc.departamentoIngeniería de Comunicaciones


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