RT Generic
T1 EEG Database for musical genres detection.
T2 Base de datos EEG para detección de géneros musicales.
A1 Ariza Cervera, Isaac
A1 Barbancho-Pérez, Ana María
A1 Tardón-García, Lorenzo José
A1 Barbancho-Pérez, Isabel
K1 Electroencefalografía
K1 Estimulación cerebral
K1 Formas musicales
AB This database is made up of EEG signals from 6 subjects listening to fragments of songs from different musical genres and their answers to the questions: did you know this song and do you like this song?. The musical genres chosen are: ballad, classic, metal and reggaeton. These signals have been captured with the BrainVision actiCHAMP-PLUS system and consist of a total of 64 EEG channels. The BrainVision Recorder software was used to store the signals. The stimulus presentation software used to design the experiment is Eprime 3.For more detailed information on this database, the capture system used and its applications, see [1].If these data are used for any publication, the following paper must be cited:[1] Isaac Ariza, Ana M. Barbancho, Lorenzo J. Tardón, Isabel Barbancho, Energy-based features and bi-LSTM neural network for EEG-based music and voice classification. Neural Comput & Applic 36, 791–802 (2024). https://doi.org/10.1007/s00521-023-09061-3
PB Universidad de Málaga
YR 2025
FD 2025-01-24
LK https://hdl.handle.net/10630/36947
UL https://hdl.handle.net/10630/36947
LA eng
NO Funding for open access publishing: Universidad Málaga/CBUA. This publication is part of Project PID2021-123207NB-I00, funded by MCIN/AEI/10.13039/501100011033/FEDER, UE. This work was partially funded by Junta de Andalucía, Proyectos de I+D+i, in the framework of Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020), under Project No. PY20_00237. Funding for open access charge: Universidad de Málaga/CBUA. This work was done at Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 23 ene 2026