EEGdash›OpenNeuro›DS008462

Iss. 8462 · 38 subjects · 40 recordings · CC0

Dataset Brief · Elena Resting-State MEG Pilot Dataset

DS008462: meg dataset, 38 subjects#

Elena Resting-State MEG Pilot Dataset

Access recordings and metadata through EEGDash.

Citation: Elena Hayday, Sara Inati, Antonio Ivano Triggiani, Afnan Jawata (2026). Elena Resting-State MEG Pilot Dataset. 10.18112/openneuro.ds008462.v1.0.2

Modality: meg Subjects: 38 Recordings: 40 License: CC0 Source: openneuro

Metadata: Complete (100%)

38-participant MEG dataset — Elena Resting-State MEG Pilot Dataset.

Data & curation Elena Hayday · Sara Inati · Antonio Ivano Triggiani · Afnan Jawata
Senior author Afnan Jawata
Year 2026 · Distributed via OpenNeuro

MEG · 330 (8), 371 (8), 370 (6), 329 (4), 327 (3), 460 (3), 326 (2), 331, 328, 350, 372, 459, 358 ch600, 1200 HzBIDS 1.9.0Task · epilepsy

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 DS008462

dataset = DS008462(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 = DS008462(cache_dir="./data", subject="01")

Advanced query

dataset = DS008462(
    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{ds008462,
  title = {Elena Resting-State MEG Pilot Dataset},
  author = {Elena Hayday and Sara Inati and Antonio Ivano Triggiani and Afnan Jawata},
  doi = {10.18112/openneuro.ds008462.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds008462.v1.0.2},
}

§ 02Study · The README

About This Dataset#

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896

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., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110.https://doi.org/10.1038/sdata.2018.110

References

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=38, range 4–54 yr, mean 26.0 yr)

05101520253035404550

Female · 16Male · 22

Sex composition

Female
16
Male
22
F : M ratio
0.73 : 1

42% female · n = 38 subjects with reported sex.

Channel counts (ch)

326327328329330331350358370371372459460

Sampling frequencies (Hz)

6001200

Total recording duration: 5 h 36 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 330 (8), 371 (8), 370 (6), 329 (4), 327 (3), 460 (3), 326 (2), 331, 328, 350, 372, 459, 358 ch · MEG · 600, 1200 Hz · 38 subjects, 40 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.DS008462(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS008462

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

class eegdash.dataset.DS008462(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Elena Resting-State MEG Pilot Dataset

Study:: ds008462 (OpenNeuro)
Author (year):: —
Canonical:: —

Also importable as: DS008462.

Modality: meg; Subject type: Unknown. Subjects: 38; recordings: 40; 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/ds008462 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds008462 DOI: https://doi.org/10.18112/openneuro.ds008462.v1.0.2

Examples

>>> from eegdash.dataset import DS008462
>>> dataset = DS008462(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 — DS008462.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 ds008462 to reproduce the tutorial on this dataset.

Citation

Elena Hayday, Sara Inati, Antonio Ivano Triggiani, Afnan Jawata (2026). Elena Resting-State MEG Pilot Dataset. 10.18112/openneuro.ds008462.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.ds008462.v1.0.2.

Related & sibling datasets

DS007870MEG · 13 subj DS008017MEG · 15 subj DS008034MEG · 12 subj ON004278MEG · 30 subj ON003568MEG · 51 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

channels · coordsystem

Provenance

CC0 · 10.18112/openneuro.ds008462.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS008462`
Title	Elena Resting-State MEG Pilot Dataset
Author (year)	—
Canonical	—
Importable as	`DS008462`
Year	2026
Authors	Elena Hayday, Sara Inati, Antonio Ivano Triggiani, Afnan Jawata
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds008462.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS008462: meg dataset, 38 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#