EEGdash›OpenNeuro›DS007666

Iss. 7666 · 55 subjects · 110 recordings · CC0

Dataset Brief · Grupos Pre and Post bids

DS007666: eeg dataset, 55 subjects#

Grupos Pre and Post bids

Citation: MENGSHA QI (—). Grupos Pre and Post bids. 10.18112/openneuro.ds007666.v1.0.0

55-participant EEG dataset — Grupos Pre and Post bids.

Data & curation MENGSHA QI

EEG · 19 (95), 21 (13), 20 (2) ch200 HzBIDS 1.6.020 tasks2 sessions

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 DS007666

dataset = DS007666(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 = DS007666(cache_dir="./data", subject="01")

Advanced query

dataset = DS007666(
    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{ds007666,
  title = {Grupos Pre and Post bids},
  author = {MENGSHA QI},
  doi = {10.18112/openneuro.ds007666.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007666.v1.0.0},
}

§ 02Study · The README

About This Dataset#

Grupos Pre and Post bids

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Sex composition

Female
31
Male
78
F : M ratio
0.40 : 1

28% female · n = 109 subjects with reported sex.

Channel counts (ch)

192021

Sampling frequencies: 200.0 Hz (n=110 recordings)

Total recording duration: 23 h 32 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 19 (95), 21 (13), 20 (2) ch · EEG · 200 Hz · 55 subjects, 110 recordings

Live trace viewer — sub-G3S008 · ses-pre · task-Grupo3Prebids

Showing one representative recording out of 55 subjects and 110 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 · 19 sensors — 19 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.DS007666(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS007666

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

class eegdash.dataset.DS007666(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Grupos Pre and Post bids

Study:: ds007666 (OpenNeuro)
Author (year):: nan
Canonical:: —

Also importable as: DS007666, nan.

Modality: eeg. Subjects: 55; recordings: 110; tasks: 20.

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/ds007666 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007666 DOI: https://doi.org/10.18112/openneuro.ds007666.v1.0.0

Examples

>>> from eegdash.dataset import DS007666
>>> dataset = DS007666(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 FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS007666.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 ds007666 to reproduce the tutorial on this dataset.

Citation

MENGSHA QI (n.d.). Grupos Pre and Post bids. 10.18112/openneuro.ds007666.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.ds007666.v1.0.0.

Related & sibling datasets

DS007667EEG · 55 subj DS007864EEG · 45 subj DS004771EEG · 61 subj DS002893EEG · 49 subj DS007688EEG · 44 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.6.0

Sidecars

events · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007666.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007666`
Title	Grupos Pre and Post bids
Author (year)	—
Canonical	—
Importable as	`DS007666`
Year	—
Authors	MENGSHA QI
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007666.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS007666: eeg dataset, 55 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#