RT Journal Article
T1 Bi-LSTM neural network for EEG-based error detection in musicians' performance
A1 Ariza, Isaac
A1 Tardon, Lorenzo J.
A1 Barbancho, Ana M.
A1 De-Torres, Irene
A1 Barbancho, Isabel
K1 Electroencephalogram (EEG)
K1 Bidirectional Long Short Term Memory
K1 (Bi-LSTM) network
K1 Mel-Frequency Cepstral Coefficients (MFCC)
K1 Musician performance
K1 Emotion recognition
K1 Enhancement
AB Electroencephalography (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.
PB Elsevier
SN 1746-8094
YR 2022
FD 2022-06-16
LK http://hdl.handle.net/10668/22067
UL http://hdl.handle.net/10668/22067
LA en
NO Ariza, 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
NO This 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
DS RISalud
RD Apr 12, 2025