Bi-LSTM Neural Network for EEG-based detection of musical characteristics

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dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.contributor.authorAriza Cervera, Isaac
dc.contributor.authorGuillén, Sergio
dc.contributor.authorTardón-García, Lorenzo José
dc.contributor.authorBarbancho-Pérez, Ana María
dc.contributor.authorBarbancho-Pérez, Isabel
dc.date.accessioned2024-01-25T11:56:41Z
dc.date.available2024-01-25T11:56:41Z
dc.date.created2024
dc.date.issued2023
dc.departamentoIngeniería de Comunicaciones
dc.description.abstractElectroencephalography (EEG) combined with Deep Learning and digital signal processing allows for mental activity recognition. In our study, a new feature extraction model-based is presented when combined with Bi-LSTM Neural Networks for EEG musical characteristics classification. This method can be used for both intra- and inter-subject scenarios reaching compelling accuracy values.es_ES
dc.description.sponsorshipThis publication is part of project PID2021-123207NB-I00, funded by MCIN / AEI / 10.13039 / 501100011033 / FEDER, UE. Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/29209
dc.language.isoenges_ES
dc.relation.eventdate12/2023es_ES
dc.relation.eventplaceSevilla, Españaes_ES
dc.relation.eventtitleAndaluz.IAes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectRedes neuronales (Informática)es_ES
dc.subjectAprendizajees_ES
dc.subjectMúsica - Estudio y enseñanzaes_ES
dc.subject.otherEEGes_ES
dc.subject.otherBI-LSTMes_ES
dc.subject.otherMusical characteristicses_ES
dc.subject.otherDeep learninges_ES
dc.titleBi-LSTM Neural Network for EEG-based detection of musical characteristicses_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication4df19151-50e7-4d01-9c10-06068cae1934
relation.isAuthorOfPublication09e99b9c-b01b-4fab-b847-367c476df65d
relation.isAuthorOfPublicationacdb2124-45a1-49ae-96dc-26bfa666e250
relation.isAuthorOfPublication.latestForDiscovery4df19151-50e7-4d01-9c10-06068cae1934

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