EEGdash›OpenNeuro›DS004502

Iss. 4502 · 48 subjects · 48 recordings · CC0

Dataset Brief · Anticipatory differences between Attention and Expectation

DS004502: eeg dataset, 48 subjects#

Anticipatory differences between Attention and Expectation

Citation: Jose M. G. Penalver, David Lopez-Garcia, Blanca Aguado-Lopez, Carlos Gonzalez-Garcia, Maria Ruz (—). Anticipatory differences between Attention and Expectation. 10.18112/openneuro.ds004502.v1.0.1

48-participant EEG dataset — Anticipatory differences between Attention and Expectation.

Data & curation Jose M. G. Penalver · David Lopez-Garcia · Blanca Aguado-Lopez · Carlos Gonzalez-Garcia · Maria Ruz

EEG · 63 (44), 65 (4) ch1000 Hz · mixedBIDS 1.2Task · attexpHealthyAttention

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 DS004502

dataset = DS004502(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 = DS004502(cache_dir="./data", subject="01")

Advanced query

dataset = DS004502(
    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{ds004502,
  title = {Anticipatory differences between Attention and Expectation},
  author = {Jose M. G. Penalver and David Lopez-Garcia and Blanca Aguado-Lopez and Carlos Gonzalez-Garcia and Maria Ruz},
  doi = {10.18112/openneuro.ds004502.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004502.v1.0.1},
}

§ 02Study · The README

About This Dataset#

No README content is available for this dataset.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=38, range 18–28 yr, mean 21.8 yr)

152025

Female · 21Male · 16Other · 1

Sex composition

Female
21
Male
16
Other
1
F : M ratio
1.31 : 1

55% female · n = 38 subjects with reported sex.

Channel counts (ch)

6365

Sampling frequencies (Hz)

5001000

Total recording duration: 92 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 63 (44), 65 (4) ch · EEG · 1000 Hz · mixed · 48 subjects, 48 recordings

Live trace viewer — sub-021 · task-attexp

Showing one representative recording out of 48 subjects and 48 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.

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.DS004502(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Penalver2023

Canonical—

Importable asDS004502 · Penalver2023

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

class eegdash.dataset.DS004502(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Anticipatory differences between Attention and Expectation

Study:: ds004502 (OpenNeuro)
Author (year):: Penalver2023
Canonical:: —

Also importable as: DS004502, Penalver2023.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 48; recordings: 48; 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/ds004502 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004502 DOI: https://doi.org/10.18112/openneuro.ds004502.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004502
>>> dataset = DS004502(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/ds004502 · pull with datasets.load_dataset("EEGDash/ds004502").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004502.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 ds004502 to reproduce the tutorial on this dataset.

Citation

Jose M. G. Penalver, David Lopez-Garcia, Blanca Aguado-Lopez, Carlos Gonzalez-Garcia, Maria Ruz (n.d.). Anticipatory differences between Attention and Expectation. 10.18112/openneuro.ds004502.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004502.v1.0.1.

Related & sibling datasets

DS007262EEG · 18 subj DS005571EEG · 24 subj DS006159EEG · 61 subj DS006317EEG · 2 subj DS004521EEG · 34 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.2

Sidecars

events · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds004502.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004502`
Title	Anticipatory differences between Attention and Expectation
Author (year)	`Penalver2023`
Canonical	—
Importable as	`DS004502`, `Penalver2023`
Year	—
Authors	Jose M. G. Penalver, David Lopez-Garcia, Blanca Aguado-Lopez, Carlos Gonzalez-Garcia, Maria Ruz
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004502.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS004502: eeg dataset, 48 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#