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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

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

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Age distribution (n=40, range 18–39 yr)

1520253035

Sex distribution

27

13

Female Male Total: 40

Channel counts: 32 ch (n=40 recordings)

Sampling frequencies: 1000.0 Hz (n=40 recordings)

Total recording duration: 15 h 33 min

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

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 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: 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)