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

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 n...

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Bibliographic Details
Published inarXiv.org
Main Authors Ariza, Isaac, Tardon, Lorenzo J, Barbancho, Ana M, De-Torres, Irene, Barbancho, Isabel
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.11.2024
Subjects
Activity recognition
Digital signal processing
Electroencephalography
Error analysis
Error detection
Machine learning
Neural networks
Neurological diseases
Temporal resolution
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ISSN2331-8422
DOI10.48550/arxiv.2411.12400

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Summary: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.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
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ISSN:2331-8422
DOI:10.48550/arxiv.2411.12400