2026-05-30T08:45:33Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/246092026-02-03T10:56:15Zcom_10630_2254col_10630_37953

Bi-LSTM neural network for EEG-based error detection in musicians’ performance Ariza Cervera, Isaac Tardón-García, Lorenzo José Barbancho-Pérez, Ana María De Torres García, Irene Barbancho-Pérez, Isabel Electroencefalografía 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. 2022-07-08T10:01:31Z 2022-07-08T10:01:31Z 2022-07-08T10:01:31Z 2022-09 journal article Isaac Ariza, Lorenzo J. Tardón, Ana M. Barbancho, Irene De-Torres, Isabel Barbancho, Bi-LSTM neural network for EEG-based error detection in musicians’ performance, Biomedical Signal Processing and Control, Volume 78, 2022, 103885, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2022.103885. https://hdl.handle.net/10630/24609 https://doi.org/10.1016/j.bspc.2022.103885. eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Elsevier