Dataset Open Access

MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music

Giorgia Cantisani; Gabriel Trégoat; Slim Essid; Gaël Richard

The MAD-EEG Dataset is a research corpus for studying EEG-based auditory attention decoding to a target instrument in polyphonic music. 

The dataset consists of 20-channel EEG responses to music recorded from 8 subjects while attending to a particular instrument in a music mixture. 

For further details, please refer to the paper: MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music.

If you use the data in your research, please reference the paper (not just the Zenodo record):

@inproceedings{Cantisani2019,
  author={Giorgia Cantisani and Gabriel Trégoat and Slim Essid and Gaël Richard},
  title={{MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music}},
  year=2019,
  booktitle={Proc. SMM19, Workshop on Speech, Music and Mind 2019},
  pages={51--55},
  doi={10.21437/SMM.2019-11},
  url={http://dx.doi.org/10.21437/SMM.2019-11}
}

 

Files (4.7 GB)
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behavioural_data.xlsx
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madeeg_preprocessed.hdf5
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madeeg_preprocessed.yaml
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madeeg_raw.hdf5
md5:795d7eea8f66550898ca2afeec55767c
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madeeg_raw.yaml
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madeeg_sequences_raw.yaml
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stimuli.zip
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tutorial-MAD-EEG.ipynb
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