DS005876#

Song Familiarity

Access recordings and metadata through EEGDash.

Citation: Jared R. Girard, Aaron M. Bishop, Cameron D. Hassall (2025). Song Familiarity. 10.18112/openneuro.ds005876.v1.0.1

Modality: eeg Subjects: 29 Recordings: 180 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005876

dataset = DS005876(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 = DS005876(cache_dir="./data", subject="01")

Advanced query

dataset = DS005876(
    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{ds005876,
  title = {Song Familiarity},
  author = {Jared R. Girard and Aaron M. Bishop and Cameron D. Hassall},
  doi = {10.18112/openneuro.ds005876.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005876.v1.0.1},
}

About This Dataset#

Song Familiarity

Twenty-nine participants listened to song melodies and responded as soon as the song felt familiar. Participants were then asked to identify the song, if possible (title, artist, or lyrics). Next, participants were shown a multiple choice display with four song titles, selected a song title, and were given visual feedback (correct: selected option turned green and a checkmark appeared next to the title; incorrect: selected option turned red and an x appeared next to the title.)

Song stimuli are taken from Kostic and Cleary (2009): https://supp.apa.org/psycarticles/supplemental/a0014584/a0014584_supp.html

An audio file with a reconstruction of what each participant heard throughout the experiment can be found in /derivatives. The audio file has been synchronized with the EEG recording.

Dataset Information#

Dataset ID	`DS005876`
Title	Song Familiarity
Year	2025
Authors	Jared R. Girard, Aaron M. Bishop, Cameron D. Hassall
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005876.v1.0.1
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: 29
Recordings: 180
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS005876 class to access this dataset programmatically.

class eegdash.dataset.DS005876(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005876. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 29; recordings: 29; 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

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/ds005876 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005876

Examples

>>> from eegdash.dataset import DS005876
>>> dataset = DS005876(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None