EEGdash›OpenNeuro›DS000247

Iss. 247 · 6 subjects · 10 recordings · CC0

Dataset Brief · MEG-BIDS OMEGA RestingState_sample

DS000247: meg dataset, 6 subjects#

MEG-BIDS OMEGA RestingState_sample

Citation: Guiomar Niso, Jeremy Moreau, Elizabeth Bock, Francois Tadel, Sylvain Baillet (2016). MEG-BIDS OMEGA RestingState_sample. 10.18112/openneuro.ds000247.v1.0.2

6-participant MEG dataset — MEG-BIDS OMEGA RestingState_sample.

Data & curation Guiomar Niso · Jeremy Moreau · Elizabeth Bock · Francois Tadel · Sylvain Baillet
Year 2016 · Distributed via OpenNeuro
Funding Quebec Bioimaging Network Strategic Initiative (QBIN 5886)

MEG · 297 (5), 330 (3), 300 (2) ch2400 HzBIDS 1.0.22 tasks6 sessionsHealthyResting StateResting-state

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 DS000247

dataset = DS000247(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 = DS000247(cache_dir="./data", subject="01")

Advanced query

dataset = DS000247(
    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{ds000247,
  title = {MEG-BIDS OMEGA RestingState_sample},
  author = {Guiomar Niso and Jeremy Moreau and Elizabeth Bock and Francois Tadel and Sylvain Baillet},
  doi = {10.18112/openneuro.ds000247.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds000247.v1.0.2},
}

§ 02Study · The README

About This Dataset#

26 MEG reference sensors (#2-#27)

270 MEG axial gradiometers (#28-#297)
1 ECG bipolar (EEG057/#298) - Not available in the empty room recordings
1 vertical EOG bipolar (EEG058/#299) - Not available in the empty room recordings
1 horizontal EOG bipolar (EEG059/#300) - Not available in the empty room recordings
- The center of the CTF coils
The anatomical references we use in Brainstorm: nasion and ears as illustrated here
Around 100 head points distributed on the hard parts of the head (no soft tissues).

OMEGA - Resting State Sample Dataset

License

This dataset was obtained from The Open MEG Archive (OMEGA, https://omega.bic.mni.mcgill.ca).

You are free to use all data in OMEGA for research purposes; please acknowledge its authors and cite the following reference in your publications if you have used data from OMEGA:

Niso G., Rogers C., Moreau J.T., Chen L.Y., Madjar C., Das S., Bock E., Tadel F., Evans A.C., Jolicoeur P., Baillet S. (2016). OMEGA: The Open MEG Archive. NeuroImage 124, 1182-1187. doi: https://doi.org/10.1016/j.neuroimage.2015.04.028. OMEGA is available at: https://omega.bic.mni.mcgill.ca

Description

View full README

OMEGA - Resting State Sample Dataset

License

This dataset was obtained from The Open MEG Archive (OMEGA, https://omega.bic.mni.mcgill.ca).

You are free to use all data in OMEGA for research purposes; please acknowledge its authors and cite the following reference in your publications if you have used data from OMEGA:

Niso G., Rogers C., Moreau J.T., Chen L.Y., Madjar C., Das S., Bock E., Tadel F., Evans A.C., Jolicoeur P., Baillet S. (2016). OMEGA: The Open MEG Archive. NeuroImage 124, 1182-1187. doi: https://doi.org/10.1016/j.neuroimage.2015.04.028. OMEGA is available at: https://omega.bic.mni.mcgill.ca

Description

Experiment - 5 subjects x 5 minute resting sessions, eyes open

MEG acquisition - Recorded at the Montreal Neurological Institute in 2012-2016 - Acquisition with CTF 275 MEG system at 2400Hz sampling rate - Anti-aliasing low-pass filter at 600Hz, files may be saved with or without the CTF 3rd order gradient compensation - Recorded channels (at least 297), include:

Subject anatomy - Structural T1 image (defaced for anonymization purposes) - Processed with FreeSurfer 5.3 - The anatomical fiducials (NAS, LPA, RPA) have already been marked and saved in the files fiducials.m

BIDS - The data in this dataset has been organized according to the MEG-BIDS specification (Brain Imaging Data Structure, http://bids.neuroimaging.io) (Niso et al. 2018) - Niso G., Gorgolewski K.J., Bock E., Brooks T.L., Flandin G., Gramfort A., Henson R.N., Jas M., Litvak V., Moreau J., Oostenveld R., Schoffelen J.M., Tadel F., Wexler J., Baillet S. (2018). MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography. Scientific Data; 5, 180110. https://doi.org/10.1038/sdata.2018.110

Release history: - 2016-12-01: initial release - 2018-07-18: release OpenNeuro ds000247 (00001 and 00002)

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=5, range 21–35 yr, mean 27.4 yr)

20253035

Female · 2Male · 3

Sex composition

Female
2
Male
3
F : M ratio
0.67 : 1

40% female · n = 5 subjects with reported sex.

Channel counts (ch)

297300330

Sampling frequencies: 2400.0 Hz (n=10 recordings)

Total recording duration: 1 h 0 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 297 (5), 330 (3), 300 (2) ch · MEG · 2400 Hz · 6 subjects, 10 recordings

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.DS000247(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Niso2018

Canonical—

Importable asDS000247 · Niso2018

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

class eegdash.dataset.DS000247(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

MEG-BIDS OMEGA RestingState_sample

Study:: ds000247 (OpenNeuro)
Author (year):: Niso2018
Canonical:: —

Also importable as: DS000247, Niso2018.

Modality: meg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 6; recordings: 10; tasks: 2.

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/ds000247 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000247 DOI: https://doi.org/10.18112/openneuro.ds000247.v1.0.2 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS000247
>>> dataset = DS000247(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/ds000247 · pull with datasets.load_dataset("EEGDash/ds000247").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS000247.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 ds000247 to reproduce the tutorial on this dataset.

Citation

Guiomar Niso, Jeremy Moreau, Elizabeth Bock, Francois Tadel, Sylvain Baillet (2016). MEG-BIDS OMEGA RestingState_sample. 10.18112/openneuro.ds000247.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.ds000247.v1.0.2.

Related & sibling datasets

DS007176EEG · 45 subj DS005420EEG · 37 subj DS003775EEG · 111 subj NM000221EEG · 19 subj DS005385EEG · 608 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.2

Sidecars

not yet probed

Provenance

CC0 · 10.18112/openneuro.ds000247.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS000247`
Title	MEG-BIDS OMEGA RestingState_sample
Author (year)	`Niso2018`
Canonical	—
Importable as	`DS000247`, `Niso2018`
Year	2016
Authors	Guiomar Niso, Jeremy Moreau, Elizabeth Bock, Francois Tadel, Sylvain Baillet
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds000247.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS000247: meg dataset, 6 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#