DS007175: eeg dataset, 41 subjects#

FFR-active-listening

Access recordings and metadata through EEGDash.

Citation: [Unspecified] (2026). FFR-active-listening. 10.18112/openneuro.ds007175.v1.0.1

Modality: eeg Subjects: 41 Recordings: 41 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007175

dataset = DS007175(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007175(cache_dir="./data", subject="01")

Advanced query

dataset = DS007175(
    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{ds007175,
  title = {FFR-active-listening},
  author = {[Unspecified]},
  doi = {10.18112/openneuro.ds007175.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007175.v1.0.1},
}

About This Dataset#

Title

Dataset Information#

Dataset ID

DS007175

Title

FFR-active-listening

Author (year)

DS7175_FFR_ActiveListening

Canonical

—

Importable as

DS007175, DS7175_FFR_ActiveListening

Year

2026

Authors

[Unspecified]

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007175.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007175,
  title = {FFR-active-listening},
  author = {[Unspecified]},
  doi = {10.18112/openneuro.ds007175.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007175.v1.0.1},
}

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: 41

  • Recordings: 41

  • Tasks: 1

Channels & sampling rate

  • Channels: 65

  • Sampling rate (Hz): 5000.0

  • Duration (hours): 46.89787261111111

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: Perception

Files & format

  • Size on disk: 200.4 GB

  • File count: 41

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007175.v1.0.1

Provenance

API Reference#

Use the DS007175 class to access this dataset programmatically.

class eegdash.dataset.DS007175(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

FFR-active-listening

Study:

ds007175 (OpenNeuro)

Author (year):

DS7175_FFR_ActiveListening

Canonical:

—

Also importable as: DS007175, DS7175_FFR_ActiveListening.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 41; recordings: 41; 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/ds007175 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007175 DOI: https://doi.org/10.18112/openneuro.ds007175.v1.0.1

Examples

>>> from eegdash.dataset import DS007175
>>> dataset = DS007175(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

See Also#