DS007647: eeg dataset, 40 subjects#

Different Doors

Access recordings and metadata through EEGDash.

Citation: Abigail Oloriz, Cameron D. Hassall (2026). Different Doors. 10.18112/openneuro.ds007647.v1.0.0

Modality: eeg Subjects: 40 Recordings: 40 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007647

dataset = DS007647(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007647(cache_dir="./data", subject="01")

Advanced query

dataset = DS007647(
    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{ds007647,
  title = {Different Doors},
  author = {Abigail Oloriz and Cameron D. Hassall},
  doi = {10.18112/openneuro.ds007647.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007647.v1.0.0},
}

About This Dataset#

Different Doors

Forty participants selected one of two doors, then received feedback indicating either a monetary gain or loss. Blocks lasted 20 trials and, unbeknownst to participants, were of two types. In learnable blocks, one of the doors was better because choosing it was associated with a 60% likelihood of a win. The other door only paid out 10% of the time. In unlearnable blocks, outcomes were not yoked to participant actions but were instead drawn from the learnable blocks and presented in random order. Thus, the win and loss totals were matched across the block types. There were 20 blocks in total (10 of each type), and each block was followed by a short survey asking which door was better, and whether the participant had fun, felt motivated, and did well.

Dataset Information#

Dataset ID

DS007647

Title

Different Doors

Author (year)

—

Canonical

—

Importable as

DS007647

Year

2026

Authors

Abigail Oloriz, Cameron D. Hassall

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007647.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007647,
  title = {Different Doors},
  author = {Abigail Oloriz and Cameron D. Hassall},
  doi = {10.18112/openneuro.ds007647.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007647.v1.0.0},
}

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: 40

  • Recordings: 40

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 15.5533

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 3.3 GB

  • File count: 40

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007647.v1.0.0

Provenance

API Reference#

Use the DS007647 class to access this dataset programmatically.

class eegdash.dataset.DS007647(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Different Doors

Study:

ds007647 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007647, nan.

Modality: eeg. Subjects: 40; recordings: 40; 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/ds007647 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007647 DOI: https://doi.org/10.18112/openneuro.ds007647.v1.0.0

Examples

>>> from eegdash.dataset import DS007647
>>> dataset = DS007647(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#