EEGdash›OpenNeuro›DS004147

Iss. 4147 · 12 subjects · 12 recordings · CC0

Dataset Brief · Average Task Value

DS004147: eeg dataset, 12 subjects#

Average Task Value

Citation: Cameron D. Hassall, Laurence T. Hunt, Clay B. Holroyd (20). Average Task Value. 10.18112/openneuro.ds004147.v1.0.2

12-participant EEG dataset — Average Task Value.

Data & curation Cameron D. Hassall · Laurence T. Hunt · Clay B. Holroyd
Year 20 · Distributed via OpenNeuro

EEG · 31 ch1000 HzBIDS 1.2.1Task · casinosHealthyVisualLearning

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#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004147

dataset = DS004147(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS004147(cache_dir="./data", subject="01")

Advanced query

dataset = DS004147(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds004147,
  title = {Average Task Value},
  author = {Cameron D. Hassall and Laurence T. Hunt and Clay B. Holroyd},
  doi = {10.18112/openneuro.ds004147.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004147.v1.0.2},
}

§ 02Study · The README

About This Dataset#

Twelve participants completed three learning tasks. In each task the goal was to learn cue-response mappings for six cues. The cues were various coloured shapes. The possible responses were left (‘d’ key) or right (‘k’ key). There were two types of cues. Low-value cues had a feedback validity of 0.5 (i.e., a coin toss). High-value cues had a feedback validity of 0.8 (80% chance of a win if the correct action was chosen). The low-value task contained only low-value cues. The high-value task contained only high-value cues. The mid-value task contained three low-value cues and three high-value cues. Participants completed 144 trials of each task.

Preprint: https://doi.org/10.1101/2021.09.16.460600

Analysis code: chassall/averagetaskvalue

Average Task Value

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=12, range 22–77 yr, mean 41.3 yr)

2030404550556575

Female · 7Male · 5

Sex composition

Female
7
Male
5
F : M ratio
1.40 : 1

58% female · n = 12 subjects with reported sex.

HandednessRight · 11Left · 1

Channel counts: 31 ch (n=12 recordings)

Sampling frequencies: 1000.0 Hz (n=12 recordings)

Total recording duration: 9 h 36 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 31 ch · EEG · 1000 Hz · 12 subjects, 12 recordings

Live trace viewer — sub-30 · task-casinos

Showing one representative recording out of 12 subjects and 12 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 · 31 sensors — 31 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

§ 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…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS004147(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Hassall2022_Average

Canonical—

Importable asDS004147 · Hassall2022_Average

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS004147(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Average Task Value

Study:: ds004147 (OpenNeuro)
Author (year):: Hassall2022_Average
Canonical:: —

Also importable as: DS004147, Hassall2022_Average.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 12; recordings: 12; 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/ds004147 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004147 DOI: https://doi.org/10.18112/openneuro.ds004147.v1.0.2 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004147
>>> dataset = DS004147(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/ds004147 · pull with datasets.load_dataset("EEGDash/ds004147").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004147.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 ds004147 to reproduce the tutorial on this dataset.

Citation

Cameron D. Hassall, Laurence T. Hunt, Clay B. Holroyd (20). Average Task Value. 10.18112/openneuro.ds004147.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004147.v1.0.2.

Related & sibling datasets

DS006802EEG · 24 subj DS004017EEG · 21 subj DS004166EEG · 71 subj DS006803EEG · 63 subj NM000339EEG · 62 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.2.1

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds004147.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004147`
Title	Average Task Value
Author (year)	`Hassall2022_Average`
Canonical	—
Importable as	`DS004147`, `Hassall2022_Average`
Year	20
Authors	Cameron D. Hassall, Laurence T. Hunt, Clay B. Holroyd
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004147.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS004147: eeg dataset, 12 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#