EEGdash›OpenNeuro›DS003822
Iss. 3822 · 25 subjects · 25 recordings · CC0
Dataset Brief · EEG

DS003822: eeg dataset, 25 subjects#

EEG: Probabilistic Learning with Affective Feedback: Exp #1

Citation: Darin R. Brown, Trevor Jackson, James F Cavanagh (20). EEG: Probabilistic Learning with Affective Feedback: Exp #1. 10.18112/openneuro.ds003822.v1.1.0

25-participant EEG dataset — EEG: Probabilistic Learning with Affective Feedback: Exp #1.

Data & curation Darin R. Brown · Trevor Jackson · James F Cavanagh
Year 20 · Distributed via OpenNeuro
500 HzBIDS 1.1.1Task · ProbabilisticSelectionHealthyVisualAffect
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 DS003822

dataset = DS003822(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003822(cache_dir="./data", subject="01")

Advanced query

dataset = DS003822(
    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{ds003822,
  title = {EEG: Probabilistic Learning with Affective Feedback: Exp #1},
  author = {Darin R. Brown and Trevor Jackson and James F Cavanagh},
  doi = {10.18112/openneuro.ds003822.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003822.v1.1.0},
}
§ 02Study · The README

About This Dataset#

RL task in N=25 college age participants. Data collected circa 2018 in the CRCL at UNM. The paper [Brown, D.R., Jackson, T.J. & Cavanagh, J.F. The Reward Positivity is sensitive to affective liking] is now coming out in Cognitive, Affective, & Behavioral Neuroscience. Your best bet for understanding this task would be to read that paper first. I’ve included additional scripts to help understand stimulus triggers etc. These additional scripts were for a secondary analysis: they were not the scripts used for the paper above. So they are slightly different and have some interesting (unfinished) tangents. - James F Cavanagh 09/29/2021

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=25, range 18–36 yr, mean 21.7 yr)

1520253035
Female · 15Male · 10

Sex composition

25
subjects
Female
15
Male
10
F : M ratio
1.50 : 1
60% female · n = 25 subjects with reported sex.

Channel counts: 66 ch (n=25 recordings)

Sampling frequencies: 500.0 Hz (n=25 recordings)

Total recording duration: 12 h 52 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage — ch · EEG · 500 Hz · 25 subjects, 25 recordings
Live trace viewer — sub-021 · task-ProbabilisticSelection

Showing one representative recording out of 25 subjects and 25 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 · 63 sensors — 63 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.

NEMAR pipeline statistics — DS003822
HED event descriptors word cloud HED event descriptors word cloud — DS003822
§ 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

DS003822

Title

EEG: Probabilistic Learning with Affective Feedback: Exp #1

Author (year)

Brown2021_Probabilistic_Learning

Canonical

—

Importable as

DS003822, Brown2021_Probabilistic_Learning

Year

20

Authors

Darin R. Brown, Trevor Jackson, James F Cavanagh

License

CC0

Citation / DOI

10.18112/openneuro.ds003822.v1.1.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003822,
  title = {EEG: Probabilistic Learning with Affective Feedback: Exp #1},
  author = {Darin R. Brown and Trevor Jackson and James F Cavanagh},
  doi = {10.18112/openneuro.ds003822.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003822.v1.1.0},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS003822(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Brown2021_Probabilistic_Learning
Canonical—
Importable asDS003822 · Brown2021_Probabilistic_Learning
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS003822(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

EEG: Probabilistic Learning with Affective Feedback: Exp

Study:

ds003822 (OpenNeuro)

Author (year):

Brown2021_Probabilistic_Learning

Canonical:

—

Also importable as: DS003822, Brown2021_Probabilistic_Learning. Subjects: 25; recordings: 25; 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/ds003822 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003822

Examples

>>> from eegdash.dataset import DS003822
>>> dataset = DS003822(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/ds003822 · pull with datasets.load_dataset("EEGDash/ds003822").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS003822.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 ds003822 to reproduce the tutorial on this dataset.

Citation

Darin R. Brown, Trevor Jackson, James F Cavanagh (20). EEG: Probabilistic Learning with Affective Feedback: Exp #1. 10.18112/openneuro.ds003822.v1.1.0

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds003822.v1.1.0.

Related & sibling datasets
DS003753— · 25 subjSame authors
BIDS
BIDS 1.1.1
Sidecars
events · events.json · channels · electrodes · coordsystem · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#