DS002725: eeg dataset, 21 subjects#

A dataset recording joint EEG-fMRI during affective music listening

Access recordings and metadata through EEGDash.

Citation: Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto (2020). A dataset recording joint EEG-fMRI during affective music listening. 10.18112/openneuro.ds002725.v1.0.0

Modality: eeg Subjects: 21 Recordings: 105 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

Dataset Information#

Dataset ID	`DS002725`
Title	A dataset recording joint EEG-fMRI during affective music listening
Author (year)	`Daly2020_joint`
Canonical	—
Importable as	`DS002725`, `Daly2020_joint`
Year	2020
Authors	Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto
License	CC0
Citation / DOI	10.18112/openneuro.ds002725.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 21
Recordings: 105
Tasks: 5

Channels & sampling rate

Channels: 46
Sampling rate (Hz): 1000.0
Duration (hours): Not calculated

Electrode Layout#

Electrode layout — EEG · 31 sensors — 31 channels

Dataset Statistics#

Age distribution (n=21, range 20–29 yr)

2025

Sex distribution

10

11

Female Male Total: 21

Channel counts: 46 ch (n=105 recordings)

Sampling frequencies: 1000.0 Hz (n=105 recordings)

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

Signal Preview#

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.

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS002725 class to access this dataset programmatically.

class eegdash.dataset.DS002725(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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)