DS003682#

Model-based aversive learning in humans is supported by preferential task state reactivation

Access recordings and metadata through EEGDash.

Citation: Toby Wise, Yunzhe Liu, Fatima Chowdhury, Raymond J. Dolan (2021). Model-based aversive learning in humans is supported by preferential task state reactivation. 10.18112/openneuro.ds003682.v1.0.0

Modality: meg Subjects: 28 Recordings: 1347 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003682

dataset = DS003682(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003682(cache_dir="./data", subject="01")

Advanced query

dataset = DS003682(
    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{ds003682,
  title = {Model-based aversive learning in humans is supported by preferential task state reactivation},
  author = {Toby Wise and Yunzhe Liu and Fatima Chowdhury and Raymond J. Dolan},
  doi = {10.18112/openneuro.ds003682.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003682.v1.0.0},
}

About This Dataset#

This dataset contains raw and processed MEG data for the paper “Model-based aversive learning in humans is supported by preferential task state reactivation” by Toby Wise, Yunzhe Liu, Fatima Chowdhury & Ray Dolan.

Raw data is provided as .fif files, although it was acquired on a CRF system.

Dataset Information#

Dataset ID

DS003682

Title

Model-based aversive learning in humans is supported by preferential task state reactivation

Year

2021

Authors

Toby Wise, Yunzhe Liu, Fatima Chowdhury, Raymond J. Dolan

License

CC0

Citation / DOI

10.18112/openneuro.ds003682.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003682,
  title = {Model-based aversive learning in humans is supported by preferential task state reactivation},
  author = {Toby Wise and Yunzhe Liu and Fatima Chowdhury and Raymond J. Dolan},
  doi = {10.18112/openneuro.ds003682.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003682.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: 28

  • Recordings: 1347

  • Tasks: 1

Channels & sampling rate

  • Channels: 272 (336), 414 (336)

  • Sampling rate (Hz): 1200.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: —

  • Type: Learning

Files & format

  • Size on disk: 211.6 GB

  • File count: 1347

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds003682.v1.0.0

Provenance

API Reference#

Use the DS003682 class to access this dataset programmatically.

class eegdash.dataset.DS003682(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003682. Modality: meg; Experiment type: Learning; Subject type: Healthy. Subjects: 28; recordings: 336; 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/ds003682 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003682

Examples

>>> from eegdash.dataset import DS003682
>>> dataset = DS003682(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#