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

Isaac Ariza; Lorenzo J. Tardon; Ana M. Barbancho; Irene De-Torres; Isabel Barbancho

doi:10.1016/j.bspc.2022.103885

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

Signal Processing 2024-11-20 v1

Authors: Isaac Ariza , Lorenzo J. Tardon , Ana M. Barbancho , Irene De-Torres , Isabel Barbancho

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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.

Keywords

eeg signal processing seizure detection and eeg analysis ecg signal processing

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@article{arxiv.2411.12400,
  title  = {Bi-LSTM neural network for EEG-based error detection in musicians' performance},
  author = {Isaac Ariza and Lorenzo J. Tardon and Ana M. Barbancho and Irene De-Torres and Isabel Barbancho},
  journal= {arXiv preprint arXiv:2411.12400},
  year   = {2024}
}

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8 pages

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

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