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

• Published in: Biomedical signal processing and control Vol. 78; p. 103885
• Main Authors: Ariza, Isaac, Tardón, Lorenzo J., Barbancho, Ana M., De-Torres, Irene, Barbancho, Isabel
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2022
• Subjects: Bidirectional Long Short Term Memory (Bi-LSTM) network; Electroencephalogram (EEG); Mel-Frequency Cepstral Coefficients (MFCC); Musician performance; Bidirectional Long Short Term Memory (Bi-LSTM) network; Electroencephalogram (EEG); Mel-Frequency Cepstral Coefficients (MFCC); Musician performance
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2022.103885

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. •Detection of performance error of music players using EEG is considered.•EEG signals are characterized by instantaneous frequency, spectral entropy and MFCCs.•A bidirectional LSTM neural network is used for error detection.•Intra- and inter-subject and instrument scenarios are addressed.•Intra- and inter-subject and instrument performance error detection can be achieved.