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dc.contributor.authorAriza Cervera, Isaac
dc.contributor.authorTardón-García, Lorenzo José 
dc.contributor.authorBarbancho-Pérez, Ana María 
dc.contributor.authorDe Torres García, Irene
dc.contributor.authorBarbancho-Pérez, Isabel 
dc.date.accessioned2022-07-08T10:01:31Z
dc.date.available2022-07-08T10:01:31Z
dc.date.issued2022-09
dc.identifier.citationIsaac Ariza, Lorenzo J. Tardón, Ana M. Barbancho, Irene De-Torres, Isabel Barbancho, Bi-LSTM neural network for EEG-based error detection in musicians’ performance, Biomedical Signal Processing and Control, Volume 78, 2022, 103885, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2022.103885.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/24609
dc.description.abstractElectroencephalography (EEG) is a tool that allows us to analyze brain activity with high temporal resolution. These measures, combined with deep learning and digital signal processing, are widely used in neurological disorder detection and emotion and mental activity recognition. In this paper, a new method for mental activity recognition is presented: instantaneous frequency, spectral entropy and Mel-frequency cepstral coefficients (MFCC) are used to classify EEG signals using bidirectional LSTM neural networks. It is shown that this method can be used for intra-subject or inter-subject analysis and has been applied to error detection in musician performance reaching compelling accuracy.es_ES
dc.description.sponsorshipThis work has been funded by Junta de Andalucía in the framework of Proyectos I+D+I en el marco del Programa Operativo FEDER Andalucia 2014–2020 under Project No.: UMA18-FEDERJA-023, Proyectos de I+D+i en el ámbito del Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020) under Project No.: PY20_00237 and Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech . Funding for open access charge: Universidad de Málaga/CBUAes_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectElectroencefalografíaes_ES
dc.subject.otherElectroencephalogram (EEG)es_ES
dc.subject.otherBidirectional Long Short Term Memoryes_ES
dc.subject.other(Bi-LSTM) networkes_ES
dc.subject.otherMel-Frequency Cepstral Coefficients (MFCC)es_ES
dc.subject.otherMusician performancees_ES
dc.titleBi-LSTM neural network for EEG-based error detection in musicians’ performancees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.identifier.doihttps://doi.org/10.1016/j.bspc.2022.103885.
dc.rights.ccAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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