DS004295#

Reward gain and punishment avoidance reversal learning

Access recordings and metadata through EEGDash.

Citation: Christopher Stolz, Alan Pickering, Erik M. Mueller (2022). Reward gain and punishment avoidance reversal learning. 10.18112/openneuro.ds004295.v1.0.0

Modality: eeg Subjects: 26 Recordings: 158 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004295

dataset = DS004295(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004295(cache_dir="./data", subject="01")

Advanced query

dataset = DS004295(
    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{ds004295,
  title = {Reward gain and punishment avoidance reversal learning},
  author = {Christopher Stolz and Alan Pickering and Erik M. Mueller},
  doi = {10.18112/openneuro.ds004295.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004295.v1.0.0},
}

About This Dataset#

Two reversal learning tasks with different reinforcer (monetary reward vs. primary threat reinforcer). Positive feedback in the reward task indicated monetary reward (+10 Cent) and negative feedback monetary non-reward (+0 Cent). In the punishment task, positive feedback signaled successful avoidance of a loud white noise burst and negative feedback the application of the noise burst. The white noise burst intensity was titrated to match monetary reward (+10 Cent) for every participant (81 dB, 84 dB, 87, dB, 90 dB).

Dataset Information#

Dataset ID

DS004295

Title

Reward gain and punishment avoidance reversal learning

Year

2022

Authors

Christopher Stolz, Alan Pickering, Erik M. Mueller

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004295.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004295,
  title = {Reward gain and punishment avoidance reversal learning},
  author = {Christopher Stolz and Alan Pickering and Erik M. Mueller},
  doi = {10.18112/openneuro.ds004295.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004295.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: 26

  • Recordings: 158

  • Tasks: 1

Channels & sampling rate

  • Channels: 66

  • Sampling rate (Hz): 1024.0 (50), 512.0 (2)

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 31.5 GB

  • File count: 158

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004295.v1.0.0

Provenance

API Reference#

Use the DS004295 class to access this dataset programmatically.

class eegdash.dataset.DS004295(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004295. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 26; recordings: 26; 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/ds004295 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004295

Examples

>>> from eegdash.dataset import DS004295
>>> dataset = DS004295(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#