folder OpenMIIR-RawEEG_v1 (11 files)
fileP01-raw.fif 683.99MB
fileP04-raw.fif 684.51MB
fileP05-raw.fif 695.84MB
fileP06-raw.fif 671.53MB
fileP07-raw.fif 695.66MB
fileP09-raw.fif 693.13MB
fileP11-raw.fif 751.16MB
fileP12-raw.fif 705.86MB
fileP13-raw.fif 713.40MB
fileP14-raw.fif 700.20MB
fileREADME.md 0.53kB
Type: Dataset
Tags: EEG, MIIR, music perception, music imagination, MIR, music information retrieval, music cognition

Bibtex:
@article{OpenMIIR-RawEEG_v1,
title = {OpenMIIR RawEEG v1.0},
journal = {},
author = {Sebastian Stober and Avital Sternin and Adrian M. Owen and Jessica A. Grahn},
year = {2015},
url = {https://github.com/sstober/openmiir},
license = {ODC PDDL},
abstract = {Music imagery information retrieval (MIIR) systems may one day be able to recognize a song just as we think of it. As a step towards such technology, we are presenting a public domain dataset of electroencephalography (EEG) recordings taken during music perception and imagination. We acquired this data during an ongoing study that so far comprised 10 subjects listening to and imagining 12 short music fragments - each 7s-16s long - taken from well-known pieces. These stimuli were selected from different genres and systematically span several musical dimensions such as meter, tempo and the presence of lyrics. This way, various retrieval and classification scenarios can be addressed. The dataset is primarily aimed to enable music information retrieval researchers interested in these new MIIR challenges to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking or tempo estimation on this new kind of data. We also hope that the OpenMIIR dataset will facilitate a stronger interdisciplinary collaboration between music information retrieval researchers and neuroscientists.}
}
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