DS004000: eeg dataset, 43 subjects#
Fribourg Ultimatum Game in Schizophrenia Study
Citation: Anna Padée, Pascal Missonnier, Anne Prévot, Grégoire Favre, Isabelle Gothuey, Marco Merlo, Jonas Richiardi (—). Fribourg Ultimatum Game in Schizophrenia Study. 10.18112/openneuro.ds004000.v1.0.0
43-participant EEG dataset — Fribourg Ultimatum Game in Schizophrenia Study.
Quickstart#
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)
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'])
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.
Cohort#
Dataset Statistics#
Channel counts: 132 ch (n=86 recordings)
Sampling frequencies: 2048.0 Hz (n=86 recordings)
Total recording duration: 12 h 3 min
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-proposer · run-1
Showing one representative recording out of
43 subjects and 86 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 128 sensors — 128 channels
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
Manifest#
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.
Full dataset metadata table
Dataset ID |
|
Title |
Fribourg Ultimatum Game in Schizophrenia Study |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Anna Padée, Pascal Missonnier, Anne Prévot, Grégoire Favre, Isabelle Gothuey, Marco Merlo, Jonas Richiardi |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004000 · Padee2022eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004000(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Fribourg Ultimatum Game in Schizophrenia Study
- Study:
ds004000(OpenNeuro)- Author (year):
Padee2022- Canonical:
—
Also importable as:
DS004000,Padee2022.Modality:
eeg. 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds004000.v1.0.0 NEMAR citation count: 6
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: 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)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004000").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds004000 to reproduce the tutorial on this dataset.
Citation
Anna Padée, Pascal Missonnier, Anne Prévot, Grégoire Favre, Isabelle Gothuey, … (n.d.). Fribourg Ultimatum Game in Schizophrenia Study. 10.18112/openneuro.ds004000.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004000.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset