DS004000#

Fribourg Ultimatum Game in Schizophrenia Study

Access recordings and metadata through EEGDash.

Citation: Anna Padée, Pascal Missonnier, Anne Prévot, Grégoire Favre, Isabelle Gothuey, Marco Merlo, Jonas Richiardi (2022). Fribourg Ultimatum Game in Schizophrenia Study. 10.18112/openneuro.ds004000.v1.0.0

Modality: eeg Subjects: 43 Recordings: 392 License: CC0 Source: openneuro Citations: 6.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004000

dataset = DS004000(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 = DS004000(cache_dir="./data", subject="01")

Advanced query

dataset = DS004000(
    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{ds004000,
  title = {Fribourg Ultimatum Game in Schizophrenia Study},
  author = {Anna Padée and Pascal Missonnier and Anne Prévot and Grégoire Favre and Isabelle Gothuey and Marco Merlo and Jonas Richiardi},
  doi = {10.18112/openneuro.ds004000.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004000.v1.0.0},
}

About This Dataset#

This is a schizophrenia in ultimatum game task study for Fribourg University. Participants were asked to play the UG in both roles, both as responder and proposer. 128 electrode EEG was recorded during the task. 19 patients with psychosis epoisodes and 24 healths controls were recorded during the task.

This dataset was recorded at the Fribourg University in Switzerland. The project was approved by the Ethics Committee of the University of Fribourg (reference number: 054/13-CER-FR).

Participants sat in a shielded room, in a comfortable chair and played the game, while EEG was recorded.

For each role, participants performed three blocks, consisting of 30 repetitions each.

Dataset Information#

Dataset ID	`DS004000`
Title	Fribourg Ultimatum Game in Schizophrenia Study
Year	2022
Authors	Anna Padée, Pascal Missonnier, Anne Prévot, Grégoire Favre, Isabelle Gothuey, Marco Merlo, Jonas Richiardi
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004000.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: 43
Recordings: 392
Tasks: 1

Channels & sampling rate

Channels: 128
Sampling rate (Hz): 2048.0
Duration (hours): 0.0

API Reference#

Use the DS004000 class to access this dataset programmatically.

class eegdash.dataset.DS004000(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004000. Modality: eeg; Experiment type: Decision-making; Subject type: Schizophrenia/Psychosis. Subjects: 43; recordings: 86; tasks: 2.

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/ds004000 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004000

Examples

>>> from eegdash.dataset import DS004000
>>> dataset = DS004000(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