EEGdash›OpenNeuro›DS004295
Iss. 4295 · 26 subjects · 26 recordings · CC0
Dataset Brief · Reward gain and punishment avoidance reversal learning

DS004295: eeg dataset, 26 subjects#

Reward gain and punishment avoidance reversal learning

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

26-participant EEG dataset — Reward gain and punishment avoidance reversal learning.

Data & curation Christopher Stolz · Alan Pickering · Erik M. Mueller
EEG · 66 ch1024 Hz · mixedBIDS 1.1.1Task · taskHealthyMultisensoryLearning
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

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},
}
§ 02Study · The README

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).

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Sex composition

26
subjects
Other
26

Channel counts: 66 ch (n=26 recordings)

Sampling frequencies (Hz)

5121024

Total recording duration: 34 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 66 ch · EEG · 1024 Hz · mixed · 26 subjects, 26 recordings
Live trace viewer — sub-s23 · task-task

Showing one representative recording out of 26 subjects and 26 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.

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

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.

NEMAR pipeline statistics — DS004295
HED event descriptors word cloud HED event descriptors word cloud — DS004295
§ 05Manifest · BIDS tree

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.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

DS004295

Title

Reward gain and punishment avoidance reversal learning

Author (year)

Stolz2022

Canonical

—

Importable as

DS004295, Stolz2022

Year

—

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},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS004295(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Stolz2022
Canonical—
Importable asDS004295 · Stolz2022
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS004295(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Reward gain and punishment avoidance reversal learning

Study:

ds004295 (OpenNeuro)

Author (year):

Stolz2022

Canonical:

—

Also importable as: DS004295, Stolz2022.

Modality: eeg. 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 DOI: https://doi.org/10.18112/openneuro.ds004295.v1.0.0 NEMAR citation count: 1

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: 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.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FacePre-bundled mirror at EEGDash/ds004295 · pull with datasets.load_dataset("EEGDash/ds004295").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS004295.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
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 ds004295 to reproduce the tutorial on this dataset.

Citation

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

BIDS
BIDS 1.1.1
Sidecars
events · channels · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#