Publication: Bi-LSTM neural network for EEG-based error detection in musicians' performance
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Date
2022-06-16
Authors
Ariza, Isaac
Tardon, Lorenzo J.
Barbancho, Ana M.
De-Torres, Irene
Barbancho, Isabel
Advisors
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Journal ISSN
Volume Title
Publisher
Elsevier
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Abstract
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.
Description
MeSH Terms
Deep Learning
Emotions
Neural Networks, Computer
Signal Processing, Computer-Assisted
Nervous System Diseases
Electroencephalography
Emotions
Neural Networks, Computer
Signal Processing, Computer-Assisted
Nervous System Diseases
Electroencephalography
DeCS Terms
Electroencefalografía
Entropía
Procesamiento de señales asistido por computador
Enfermedades del sistema nervioso
Emociones
Encéfalo
Entropía
Procesamiento de señales asistido por computador
Enfermedades del sistema nervioso
Emociones
Encéfalo
CIE Terms
Keywords
Electroencephalogram (EEG), Bidirectional Long Short Term Memory, (Bi-LSTM) network, Mel-Frequency Cepstral Coefficients (MFCC), Musician performance, Emotion recognition, Enhancement
Citation
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