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

TL;DR

This paper introduces a novel EEG-based method utilizing bidirectional LSTM neural networks with spectral features for detecting errors in musicians' performances, demonstrating high accuracy in intra- and inter-subject analysis.

Contribution

The paper presents a new approach combining spectral features and Bi-LSTM for error detection in musical performance using EEG signals, which is a novel application.

Findings

Effective error detection in musicians' EEG signals.

High accuracy in intra- and inter-subject analysis.

Potential for real-time performance monitoring.

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.