2026-05-31T21:05:43Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/369472026-02-03T11:37:16Zcom_10630_2254col_10630_20092

EEG Database for musical genres detection. Ariza Cervera, Isaac Barbancho-Pérez, Ana María Tardón-García, Lorenzo José Barbancho-Pérez, Isabel Electroencefalografía Estimulación cerebral Formas musicales 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 2025-01-24T13:08:17Z 2025-01-24T13:08:17Z 2025-01-24T13:08:17Z 2025-01-24 dataset https://hdl.handle.net/10630/36947 10.24310/riuma.36947 eng 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 http://creativecommons.org/licenses/by-nc/4.0/ open access Attribution-NonCommercial 4.0 Internacional Universidad de Málaga