EEGdash›OpenNeuro›DS003474

Iss. 3474 · 122 subjects · 122 recordings · CC0

Dataset Brief · EEG

DS003474: eeg dataset, 122 subjects#

EEG: Probabilistic Selection and Depression

Citation: James F Cavanagh jcavanagh@unm.edu (20). EEG: Probabilistic Selection and Depression. 10.18112/openneuro.ds003474.v1.1.0

122-participant EEG dataset — EEG: Probabilistic Selection and Depression.

Data & curation James F Cavanagh jcavanagh@unm.edu
Year 20 · Distributed via OpenNeuro

EEG · 66 (72), 67 (50) ch500 HzBIDS 1.1.1Task · ProbabilisticSelectionHealthyVisualDecision-making

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 DS003474

dataset = DS003474(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 = DS003474(cache_dir="./data", subject="01")

Advanced query

dataset = DS003474(
    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{ds003474,
  title = {EEG: Probabilistic Selection and Depression},
  author = {James F Cavanagh jcavanagh@unm.edu},
  doi = {10.18112/openneuro.ds003474.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003474.v1.1.0},
}

§ 02Study · The README

About This Dataset#

Probabilistic selection task with 122 college-age participants. Task included in DMDX programming language. Data collected circa 2008-2010 in John J.B. Allen lab at U Arizona. Subjects scored reliably high or low in Beck Depression Inventory. Some have been clinically interviewed. For some subjects (maybe all?), HEOG and VEOG may be mis-labeled as the other. Some files have had some channels interpolated already. There are no raw data to revert to instead… Note subj 544 is not used b/c they had unstable BDI from pre-assessment to test session. Code is included to re-create this paper: DOI: 10.1162/cpsy_a_00024

James F Cavanagh 01/11/2021

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=120, range 18–24 yr, mean 18.9 yr)

1520

Other · 120

Sex composition

Other
121

Channel counts (ch)

6667

Sampling frequencies: 500.0 Hz (n=122 recordings)

Total recording duration: 36 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 66 (72), 67 (50) ch · EEG · 500 Hz · 122 subjects, 122 recordings

Live trace viewer — sub-021 · task-ProbabilisticSelection

Showing one representative recording out of 122 subjects and 122 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 · 64 sensors — 64 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.DS003474(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Cavanagh2021_Probabilistic

Canonical—

Importable asDS003474 · Cavanagh2021_Probabilistic

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

class eegdash.dataset.DS003474(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

EEG: Probabilistic Selection and Depression

Study:: ds003474 (OpenNeuro)
Author (year):: Cavanagh2021_Probabilistic
Canonical:: —

Also importable as: DS003474, Cavanagh2021_Probabilistic.

Modality: eeg. Subjects: 122; recordings: 122; 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/ds003474 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003474 DOI: https://doi.org/10.18112/openneuro.ds003474.v1.1.0 NEMAR citation count: 9

Examples

>>> from eegdash.dataset import DS003474
>>> dataset = DS003474(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/ds003474 · pull with datasets.load_dataset("EEGDash/ds003474").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS003474.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 ds003474 to reproduce the tutorial on this dataset.

Citation

James F Cavanagh jcavanagh@unm.edu (20). EEG: Probabilistic Selection and Depression. 10.18112/openneuro.ds003474.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.ds003474.v1.1.0.

Related & sibling datasets

DS007012EEG · 117 subj DS003478EEG · 122 subjSame authors DS004532EEG · 110 subjSame authors DS003518EEG · 110 subjSame authors DS004579EEG · 139 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.1.1

Sidecars

events · events.json · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds003474.v1.1.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS003474`
Title	EEG: Probabilistic Selection and Depression
Author (year)	`Cavanagh2021_Probabilistic`
Canonical	—
Importable as	`DS003474`, `Cavanagh2021_Probabilistic`
Year	20
Authors	James F Cavanagh jcavanagh@unm.edu
License	CC0
Citation / DOI	10.18112/openneuro.ds003474.v1.1.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS003474: eeg dataset, 122 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#