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Bi-LSTM Neural Network for EEG-based detection of musical characteristics
dc.contributor.author | Ariza Cervera, Isaac | |
dc.contributor.author | Guillén, Sergio | |
dc.contributor.author | Tardón-García, Lorenzo José | |
dc.contributor.author | Barbancho-Pérez, Ana María | |
dc.contributor.author | Barbancho-Pérez, Isabel | |
dc.date.accessioned | 2024-01-25T11:56:41Z | |
dc.date.available | 2024-01-25T11:56:41Z | |
dc.date.created | 2024 | |
dc.date.issued | 2023 | |
dc.identifier.uri | https://hdl.handle.net/10630/29209 | |
dc.description.abstract | Electroencephalography (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.sponsorship | This 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.language.iso | eng | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Redes neuronales (Informática) | es_ES |
dc.subject | Aprendizaje | es_ES |
dc.subject | Música-Estudio y enseñanza | es_ES |
dc.subject.other | EEG | es_ES |
dc.subject.other | BI-LSTM | es_ES |
dc.subject.other | Musical characteristics | es_ES |
dc.subject.other | Deep learning | es_ES |
dc.title | Bi-LSTM Neural Network for EEG-based detection of musical characteristics | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.centro | E.T.S.I. Telecomunicación | es_ES |
dc.relation.eventtitle | Andaluz.IA | es_ES |
dc.relation.eventplace | Sevilla, España | es_ES |
dc.relation.eventdate | 12/2023 | es_ES |
dc.rights.cc | Atribución 4.0 Internacional | * |