DS006735: eeg dataset, 27 subjects#
Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding
Citation: Tong Shan, Edmund C. Lalor, Ross K. Maddox (2024). Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding. 10.18112/openneuro.ds006735.v2.0.0
27-participant EEG dataset — Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006735
dataset = DS006735(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006735(cache_dir="./data", subject="01")
Advanced query
dataset = DS006735(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{ds006735,
title = {Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding},
author = {Tong Shan and Edmund C. Lalor and Ross K. Maddox},
doi = {10.18112/openneuro.ds006735.v2.0.0},
url = {https://doi.org/10.18112/openneuro.ds006735.v2.0.0},
}
About This Dataset#
Please contact the following authors for further information:
Tong Shan (email: tongshan@stanford.edu)
Ross K. Maddox (email: rkmaddox@med.umich.edu)
Details related to access to the data
Overview
This study examines pitch-time interactions in music processing by introducing “chimeric music,” which pairs two distinct melodies, and exchanges their pitch contours and note onset-times to create two new melodies, thereby distorting musical pattern while maintaining the marginal statistics of the original pieces’ pitch and temporal sequences.
Data collected from Sep to Nov, 2023. The details of the experiment can be found at Shan et al. (2024). There were two phases in this experiment. For the first phase, ten trials of one-minute clicks were presented to the subjects. For the second phase, the 2 types of monophonic music (original and chimeric) clips were presented. There were 33 trials for each type with shuffled order. Between trials, there was a 0.5 s pause.
The code for analysis for this study can be found in GitHub repo (maddoxlab/Chimeric_music).
View full README
Details related to access to the data
Overview
This study examines pitch-time interactions in music processing by introducing “chimeric music,” which pairs two distinct melodies, and exchanges their pitch contours and note onset-times to create two new melodies, thereby distorting musical pattern while maintaining the marginal statistics of the original pieces’ pitch and temporal sequences.
Data collected from Sep to Nov, 2023. The details of the experiment can be found at Shan et al. (2024). There were two phases in this experiment. For the first phase, ten trials of one-minute clicks were presented to the subjects. For the second phase, the 2 types of monophonic music (original and chimeric) clips were presented. There were 33 trials for each type with shuffled order. Between trials, there was a 0.5 s pause.
The code for analysis for this study can be found in GitHub repo (maddoxlab/Chimeric_music).
Format
This dataset is formatted according to the EEG Brain Imaging Data Structure. It includes EEG recording from subject 001 to subject 027 in raw brainvision format (including .eeg, .vhdr, and .vmrk triplet).
Subjects
27 subjects participated in this study.
Subject inclusion criteria
Age between 18-40.
Normal hearing: audiometric thresholds of 20 dB HL or better from 500 to 8000 Hz. Speak English as their primary language.
Self-reported normal or correctable to normal vision. Twenty-seven participants participated in this experiment with an age of 22.9 ± 3.9 (mean ± STD) years.
Apparatus
Subjects were seated in a sound-isolating booth on a chair in front of a 24-inch BenQ monitor with a viewing distance of approximately 60 cm. Stimuli were presented at an average level of 60 dB SPL and a sampling rate of 48000 Hz through ER-2 insert earphones plugged into an RME Babyface Pro digital sound card. The stimulus presentation for the experiment was controlled by a python script using a custom package, expyfun.
Following the experimental session, participants completed a self-reported musicianship questionnaire (adapted from Whiteford et al, 2025). The questionnaire is included in this repository.
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies: 10000.0 Hz (n=27 recordings)
Total recording duration: 38 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-ChimericMusic
Showing one representative recording out of
27 subjects and 27 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 36 sensors — 36 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2024 |
Authors |
Tong Shan, Edmund C. Lalor, Ross K. Maddox |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006735,
title = {Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding},
author = {Tong Shan and Edmund C. Lalor and Ross K. Maddox},
doi = {10.18112/openneuro.ds006735.v2.0.0},
url = {https://doi.org/10.18112/openneuro.ds006735.v2.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006735 · Shan2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006735(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding
- Study:
ds006735(OpenNeuro)- Author (year):
Shan2025- Canonical:
—
Also importable as:
DS006735,Shan2025.Modality:
eeg; Experiment type:Perception; Subject type:Healthy. Subjects: 27; recordings: 27; tasks: 1.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds006735 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006735 DOI: https://doi.org/10.18112/openneuro.ds006735.v2.0.0
Examples
>>> from eegdash.dataset import DS006735 >>> dataset = DS006735(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds006735").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds006735 to reproduce the tutorial on this dataset.
Citation
Tong Shan, Edmund C. Lalor, Ross K. Maddox (2024). Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding. 10.18112/openneuro.ds006735.v2.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds006735.v2.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset