September 25, 2019 / by Giorgia Cantisani / MAD-EEG

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

MAD-EEG is a research corpus for studying the problem of EEG-based auditory attention decoding for music. It contains 20-channel EEG responses to music recorded while the subjects were attending to a particular instrument in a music mixture.  The stimuli were designed considering variations in terms of number and type of instruments in the mixture, spatial rendering, music genre and melody that is played.

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The dataset is available from Zenodo.

For more details, please refer to the paper MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music by Cantisani G. et al., SMM, 2019.

This project is part of my PhD Thesis conducted at Télécom Paris under the supervision of Professor Slim Essid and Gaël Richard.