DS002725: eeg dataset, 21 subjects#
A dataset recording joint EEG-fMRI during affective music listening
Citation: Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto (2019). A dataset recording joint EEG-fMRI during affective music listening. 10.18112/openneuro.ds002725.v1.0.0
21-participant EEG dataset — A dataset recording joint EEG-fMRI during affective music listening.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS002725
dataset = DS002725(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS002725(cache_dir="./data", subject="01")
Advanced query
dataset = DS002725(
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{ds002725,
title = {A dataset recording joint EEG-fMRI during affective music listening},
author = {Ian Daly and Nicoletta Nicolaou and Duncan Williams and Faustina Hwang and Alexis Kirke and Eduardo Miranda and Slawomir J. Nasuto},
doi = {10.18112/openneuro.ds002725.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds002725.v1.0.0},
}
About This Dataset#
Dataset: Joint EEG-fMRI recording during affective music listening.
This dataset was recorded from 21 healthy adult participants viia a joint EEG-fMRI modality while they
listened to a set of music stimuli chosen and generated to produce different affective (emotional) reponses. Participants self-reported their felt affective states as they listened to the music.
The full experiment description can be found in our paper (Daly et.al., 2019). Data recorded in 2016 Published in 2019 [1] Daly, I., Williams, D., Hwang, F., Kirke, A., Miranda, E. R., & Nasuto, S. J. (2019). Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music. Scientific Reports, 9(1), 9415. https://doi.org/10.1038/s41598-019-45105-2
Cohort#
Dataset Statistics#
Age distribution by gender (n=21, range 20–29 yr, mean 24.3 yr)
Sex composition
Channel counts: 46 ch (n=105 recordings)
Sampling frequencies: 1000.0 Hz (n=105 recordings)
Total recording duration: 22 h 32 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-classicalMusic
Showing one representative recording out of
21 subjects and 105 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 · 31 sensors — 31 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 |
A dataset recording joint EEG-fMRI during affective music listening |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds002725,
title = {A dataset recording joint EEG-fMRI during affective music listening},
author = {Ian Daly and Nicoletta Nicolaou and Duncan Williams and Faustina Hwang and Alexis Kirke and Eduardo Miranda and Slawomir J. Nasuto},
doi = {10.18112/openneuro.ds002725.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds002725.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS002725 · Daly2020_jointeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS002725(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
A dataset recording joint EEG-fMRI during affective music listening
- Study:
ds002725(OpenNeuro)- Author (year):
Daly2020_joint- Canonical:
—
Also importable as:
DS002725,Daly2020_joint.Modality:
eeg. Subjects: 21; recordings: 105; tasks: 5.- 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/ds002725 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002725 DOI: https://doi.org/10.18112/openneuro.ds002725.v1.0.0 NEMAR citation count: 2
Examples
>>> from eegdash.dataset import DS002725 >>> dataset = DS002725(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/ds002725").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 ds002725 to reproduce the tutorial on this dataset.
Citation
Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, … (2019). A dataset recording joint EEG-fMRI during affective music listening. 10.18112/openneuro.ds002725.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds002725.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset