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Bi-LSTM neural network for EEG-based error detection in musicians' performance

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dc.contributor.authorAriza, Isaac
dc.contributor.authorTardon, Lorenzo J.
dc.contributor.authorBarbancho, Ana M.
dc.contributor.authorDe-Torres, Irene
dc.contributor.authorBarbancho, Isabel
dc.contributor.authoraffiliation[Ariza, Isaac] Univ Malaga, AT Res Grp, ETSI Telecomunicac, Malaga 29071, Spain
dc.contributor.authoraffiliation[Tardon, Lorenzo J.] Univ Malaga, AT Res Grp, ETSI Telecomunicac, Malaga 29071, Spain
dc.contributor.authoraffiliation[Barbancho, Ana M.] Univ Malaga, AT Res Grp, ETSI Telecomunicac, Malaga 29071, Spain
dc.contributor.authoraffiliation[Barbancho, Isabel] Univ Malaga, AT Res Grp, ETSI Telecomunicac, Malaga 29071, Spain
dc.contributor.authoraffiliation[De-Torres, Irene] Hosp Reg Univ Malaga, Ave Carlos Haya 84, Malaga 29010, Spain
dc.contributor.funderJunta de Andalucia
dc.contributor.funderUniversidad de Malaga, Campus de Excelencia Internacional Andalucia Tech
dc.contributor.funderUniversidad de Malaga/CBUA
dc.date.accessioned2023-05-03T14:49:15Z
dc.date.available2023-05-03T14:49:15Z
dc.date.issued2022-06-16
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.
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/CBUA
dc.description.versionSi
dc.identifier.citationAriza, I., Tardón, L. J., Barbancho, A. M., De-Torres, I., & Barbancho, I. (2022). Bi-LSTM neural network for EEG-based error detection in musicians’ performance. Biomedical Signal Processing And Control, 78, 103885
dc.identifier.doi10.1016/j.bspc.2022.103885
dc.identifier.essn1746-8108
dc.identifier.issn1746-8094
dc.identifier.unpaywallURLhttps://doi.org/10.1016/j.bspc.2022.103885
dc.identifier.urihttp://hdl.handle.net/10668/22067
dc.identifier.wosID814284600006
dc.journal.titleBiomedical signal processing and control
dc.journal.titleabbreviationBiomed. signal process. control
dc.language.isoen
dc.organizationHospital Universitario Regional de Málaga
dc.page.number8
dc.provenanceRealizada la curación de contenido 19/02/2025
dc.publisherElsevier
dc.relation.projectIDUMA18-FEDERJA-023
dc.relation.projectIDPY20_00237
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1746809422003950
dc.rightsAttribution 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectElectroencephalogram (EEG)
dc.subjectBidirectional Long Short Term Memory
dc.subject(Bi-LSTM) network
dc.subjectMel-Frequency Cepstral Coefficients (MFCC)
dc.subjectMusician performance
dc.subjectEmotion recognition
dc.subjectEnhancement
dc.subject.decsElectroencefalografía
dc.subject.decsEntropía
dc.subject.decsProcesamiento de señales asistido por computador
dc.subject.decsEnfermedades del sistema nervioso
dc.subject.decsEmociones
dc.subject.decsEncéfalo
dc.subject.meshDeep Learning
dc.subject.meshEmotions
dc.subject.meshNeural Networks, Computer
dc.subject.meshSignal Processing, Computer-Assisted
dc.subject.meshNervous System Diseases
dc.subject.meshElectroencephalography
dc.titleBi-LSTM neural network for EEG-based error detection in musicians' performance
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number78
dc.wostypeArticle
dspace.entity.typePublication

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