Bi-LSTM neural network for EEG-based error detection in musicians' performance. (September 2022)

• Record Type: Journal Article
• Authors: Ariza, Isaac; Tardón, Lorenzo J.; Barbancho, Ana M.; De-Torres, Irene; Barbancho, Isabel
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Electroencephalogram (EEG) -- Bidirectional Long Short Term Memory (Bi-LSTM) network -- Mel-Frequency Cepstral Coefficients (MFCC) -- Musician performance; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103885
• Languages: English
• ISSNs: 1746-8094

Abstract

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. Highlights: 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.