DOI: 10.5061/dryad.g1jwstqmh
title: Cortical encoding of melodic expectations in human temporal cortex

description: Humans engagement in music rests on underlying elements such as the
 listeners’ cultural background and interest in music. These factors
 modulate how listeners anticipate musical events, a process inducing
 instantaneous neural responses as the music confronts these expectations.
 Measuring such neural correlates would represent a direct window into
 high-level brain processing. Here we recorded cortical signals as
 participants listened to Bach melodies. We assessed the relative
 contributions of acoustic versus melodic components of the music to the
 neural signal. Melodic features included information on pitch progressions
 and their tempo, which were extracted from a predictive model of musical
 structure based on Markov chains. We related the music to brain activity
 with temporal response functions demonstrating, for the first time,
 distinct cortical encoding of pitch and note-onset expectations during
 naturalistic music listening. This encoding was most pronounced at
 response latencies up to 350ms, and in both planum temporale and Heschl’s
 gyrus.
description: ----------------------------------------------------------------------------------------------------<br> --------------------------------------- DiliBach EEG dataset --------------------------------------- Monophonic Bach music EEG Experiment from:<br> Di Liberto et al., Cortical encoding of melodic expectations in human temporal cortex, eLife, 2020 The EEG experiment was designed by Giovanni M. Di Liberto and Claire Pelofi<br> and conducted at the Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, Ecole Normale Supérieure,<br> PSL University, France.<br> Data were collected by Giovanni M. Di Liberto, Claire Pelofi, and Gaelle Rouvier<br> (See below for the list of authors and affiliations) Version 1.2: 24 June 2021 - Giovanni M. Di Liberto --- CND data --------------------------------------------------------------------------------------- EEG signal was recorded as participants listened to monophonic piano Bach music.<br> Subjects 1-10 were non-musicians and 11-20 were expert pianists.<br> One Matlab (.mat) file for each subject. Each data file contains two variables: 'fs' (the sampling<br> frequency) and the EEG data. --- Stimuli ----------------------------------------------------------------------------------------<br> Original stimuli can be found on http://www.jsbach.net<br> and correspond to Bach violin pieces from sonatas and partitas, and partita for flute in A minor. Audio files original filenames<br> audio1.mid - fp-1all.mid<br> audio2.mid - fp-2cou.mid<br> audio3.mid - fp-3sar.mid<br> audio4.mid - fp-4bou.mid<br> audio5.mid - vp2-1all.mid<br> audio6.mid - vs1-4prs.mid<br> audio7.mid - vp1-1al_v2.mid<br> audio8.mid - vp2-4gig_v2.mid<br> audio9.mid - vp3-2lou.mid<br> audio10.mid - vp3-3gav_v2.mid
description: EEG data were saved according to the CND data format
 (Continuous-events Neural Data format;
 www.cnspworkshop.net/cndFormat.html). A single file is
 saved containing the stimulus data, which is the same across all subjects.
 This file contains a variable 'stim' including, for example,
 stimulus features such as the speech envelope. A single
 file per subject contains the subject-specific EEG data. The experiment
 was grouped into 30 trials (each playing one of the ten available pieces).
 The 30 trials are collectively stored using a cell array. The trial order
 is the same as in the 'stim' variable (so the same order across
 participants), and it includes a variable indicating the original
 presentation order for each subject. Key variables and
 key fields in the CND structures: *** dataStim.mat:
 Information and data that is common across subjects (in the CND
 format)<br>  stim.data{1,2}: speech envelope vectors for the second
 piece<br>  stim.data{2,5}: note onset vector for the fifth
 piece<br>  stim.data{3,5}: note onset vectors modulated by the
 absolute pitch value for the fifth piece<br>  stim.fs: Envelope
 signal sampling frequency *** dataSub1.mat, ...,
 dataSubN.mat: Subject-specific EEG data (in the CND format)<br>
  eeg.fs: EEG sampling frequency (512 Hz)<br>  eeg.data: cell array
 with the EEG signal for each of the 30 experimental trials
 (corresponding<br>            to the 30 cells in the dataStim.mat
 file. Each cell has size: timeSamples x channels<br>  eeg.chanlocs:
 channel location information<br>  eeg.extChan{1}.data: EEG external
 mastoid electrodes<br>  eeg.paddingStartSample: EEG data were
 time-locked to the onset of the speech stimulus for each<br>
                          trial. However, note that a padding was added at
 the start and end of<br>                          each trial to
 allow the users to, for example, perform filtering and<br>
                          then remove side filtering artifacts if needed.
 Of course, this also<br>                          means that the
 first sample of the stimulus corresponds to the EEG<br>
                          sample eeg.paddingStartSample+1 (=513).
 Notes:<br>  - There were 64 scalp channels + 2 mastoid
 electrodes (electrodes 65 and 66 correspond to left and right mastoids
 respectively);<br>  - Stimulus vectors were shared at fs=64Hz, as in
 Di Liberto et al., eLife, 2020. In addition, we have shared the original
 MIDI files used for the experiment (see README file).