EEGdash›OpenNeuro›DS000248

Iss. 248 · 2 subjects · 3 recordings · CC0

Dataset Brief · MNE-Sample-Data

DS000248: meg dataset, 2 subjects#

MNE-Sample-Data

Citation: Alexandre Gramfort, Matti S Hämäläinen (2019). MNE-Sample-Data. 10.18112/openneuro.ds000248.v1.2.4

2-participant MEG dataset — MNE-Sample-Data.

Data & curation Alexandre Gramfort · Matti S Hämäläinen
Year 2019 · Distributed via OpenNeuro
Funding NIH 5R01EB009048 · NIH 1R01EB009048 · + 9 more

MEG · 315, 376 ch601 HzBIDS 1.4.02 tasksHealthyMultisensoryAttention

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 DS000248

dataset = DS000248(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 = DS000248(cache_dir="./data", subject="01")

Advanced query

dataset = DS000248(
    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{ds000248,
  title = {MNE-Sample-Data},
  author = {Alexandre Gramfort and Matti S Hämäläinen},
  doi = {10.18112/openneuro.ds000248.v1.2.4},
  url = {https://doi.org/10.18112/openneuro.ds000248.v1.2.4},
}

§ 02Study · The README

About This Dataset#

The MNE software is accompanied by a sample data set. These data were acquired with the Neuromag Vectorview system at MGH/HMS/MIT Athinoula A. Martinos Center Biomedical Imaging. EEG data from a 60-channel electrode cap was acquired simultaneously with the MEG. The original MRI data set was acquired with a Siemens 1.5 T Sonata scanner using an MPRAGE sequence.

In the MEG/EEG experiment, checkerboard patterns were presented into the left and right visual field, interspersed by tones to the left or right ear. The interval between the stimuli was 750 ms. Occasionally a smiley face was presented at the center of the visual field. The subject was asked to press a key with the right index finger as soon as possible after the appearance of the face.

MNE-Sample-Data

Freesurfer derivatives

Calls from the command line: - recon-all -i sub-01/anat/sub-01_T1w.nii.gz -s sub-01 -all - mne make_scalp_surfaces -s sub-01 --overwrite --force - mne flash_bem -s sub-01 --overwrite - mne watershed_bem -s sub-01 --overwrite

References

A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, M. Hämäläinen, MNE software for processing MEG and EEG data, NeuroImage, Volume 86, 1 February 2014, Pages 446-460, ISSN 1053-8119 A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, R. Goj, M. Jas, T. Brooks, L. Parkkonen, M. Hämäläinen, MEG and EEG data analysis with MNE-Python, Frontiers in Neuroscience, Volume 7, 2013, ISSN 1662-453X” 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. http://doi.org/10.1038/sdata.2018.110

References

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

315376

Sampling frequencies: 600.614990234375 Hz (n=2 recordings)

Total recording duration: 6 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 315, 376 ch · MEG · 601 Hz · 2 subjects, 3 recordings

Live trace viewer — sub-01 · task-audiovisual · run-01

Showing one representative recording out of 2 subjects and 3 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _meg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?meg=<url>) to inspect it.

Electrode layout — MEG · 306 sensors — 306 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.DS000248(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Gramfort2018

Canonical—

Importable asDS000248 · Gramfort2018

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

class eegdash.dataset.DS000248(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

MNE-Sample-Data

Study:: ds000248 (OpenNeuro)
Author (year):: Gramfort2018
Canonical:: —

Also importable as: DS000248, Gramfort2018.

Modality: meg; Experiment type: Attention; Subject type: Healthy. Subjects: 2; recordings: 3; 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/ds000248 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000248 DOI: https://doi.org/10.18112/openneuro.ds000248.v1.2.4 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS000248
>>> dataset = DS000248(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/ds000248 · pull with datasets.load_dataset("EEGDash/ds000248").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS000248.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 ds000248 to reproduce the tutorial on this dataset.

Citation

Alexandre Gramfort, Matti S Hämäläinen (2019). MNE-Sample-Data. 10.18112/openneuro.ds000248.v1.2.4

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds000248.v1.2.4.

Related & sibling datasets

DS003633MEG · 12 subj DS003922MEG · 14 subj DS006334MEG · 30 subj DS004107MEG · 9 subj DS005346MEG · 30 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.4.0

Sidecars

coordsystem

Provenance

CC0 · 10.18112/openneuro.ds000248.v1.2.4

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS000248`
Title	MNE-Sample-Data
Author (year)	`Gramfort2018`
Canonical	—
Importable as	`DS000248`, `Gramfort2018`
Year	2019
Authors	Alexandre Gramfort, Matti S Hämäläinen
License	CC0
Citation / DOI	10.18112/openneuro.ds000248.v1.2.4
Source links	OpenNeuro \| NeMAR \| Source URL

DS000248: meg dataset, 2 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#