EEGdash›OpenNeuro›DS003392

Iss. 3392 · 12 subjects · 33 recordings · CC0

Dataset Brief · NeuroSpin hMT+ Localizer DATA (MEG & aMRI)

DS003392: meg dataset, 12 subjects#

NeuroSpin hMT+ Localizer DATA (MEG & aMRI)

Citation: Nicolas Zilber, Philippe Ciuciu, Alexandre Gramfort, Leila Azizi, Virginie van Wassenhove (2014). NeuroSpin hMT+ Localizer DATA (MEG & aMRI). 10.18112/openneuro.ds003392.v1.0.4

12-participant MEG dataset — NeuroSpin hMT+ Localizer DATA (MEG & aMRI).

Data & curation Nicolas Zilber · Philippe Ciuciu · Alexandre Gramfort · Leila Azizi · Virginie van Wassenhove
Year 2014 · Distributed via OpenNeuro
Funding This work was supported by a Marie Curie IRG-249222 and an ERC-StG-263584 to V.vW and an ANR Schubert ANR-0909-JCJC-071 to P.C.

MEG · 320 ch2000 HzBIDS ?2 tasks11 sessionsHealthyVisualPerception

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 DS003392

dataset = DS003392(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 = DS003392(cache_dir="./data", subject="01")

Advanced query

dataset = DS003392(
    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{ds003392,
  title = {NeuroSpin hMT+ Localizer DATA (MEG & aMRI)},
  author = {Nicolas Zilber and Philippe Ciuciu and Alexandre Gramfort and Leila Azizi and Virginie van Wassenhove},
  doi = {10.18112/openneuro.ds003392.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds003392.v1.0.4},
}

§ 02Study · The README

About This Dataset#

Dataset description: Magnetoencephalography (MEG) dataset recorded during a hMT+ (human visual motion area) localizer task

Published in:

Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

Data curation: Sophie Herbst, Alexandre Gramfort This MEG dataset was prepared in the Brain Imaging Data Structure (MEG-BIDS, Niso et al. 2018) format using MNE-BIDS (Appelhoff et al. 2019).

The dataset contains 10 of the 12 participants from the vision-only training group. Two participants were removed, one due to problems with the trigger channel, and one due to different settings in the acquisition preventing us from processing the dataset without prior adjustment.

EXPERIMENT

Participants were presented with a cloud of moving dots, always starting with incoherent movement (up or down result in equal display, due to the incoherence).

View full README

Data curation: Sophie Herbst, Alexandre Gramfort This MEG dataset was prepared in the Brain Imaging Data Structure (MEG-BIDS, Niso et al. 2018) format using MNE-BIDS (Appelhoff et al. 2019).

The dataset contains 10 of the 12 participants from the vision-only training group. Two participants were removed, one due to problems with the trigger channel, and one due to different settings in the acquisition preventing us from processing the dataset without prior adjustment.

EXPERIMENT

Participants were presented with a cloud of moving dots, always starting with incoherent movement (up or down result in equal display, due to the incoherence).

After 500 ms, the movement became coherent in 50% of the trials (95% coherence, up or down) and remained incoherent in the other 50%, lasting for 1000 ms. Participants were instructed to passively view the stimuli for a total of 120 trials. Events: 1: coherent / down 2: coherent / up 3: incoherent / down 4: incoherent / up

MEG

Brain magnetic fields were recorded in a MSR using a 306 MEG system (Neuromag Elekta LTD, Helsinki). MEG recordings were sampled at 2 kHz and band-pass filtered between 0.03 and 600 Hz.

Four head position coils (HPI) measured the head position of participants before each block; three fiducial markers (nasion and pre-auricular points) were used for digitization and anatomicalMRI (aMRI) immediately following MEG acquisition.

Electrooculograms (EOG, horizontal and vertical eye movements) and electrocardiogram (ECG) were simultaneously recorded.

Prior to the session, 5 min of empty room recordings was acquired for the computation of the noise covariance matrix.

Bad MEG channels were marked manually.

MRI

The T1 weighted aMRI was recorded using a 3-T Siemens Trio MRI scanner. Parameters of the sequence were: voxel size: 1.0 × 1.0 × 1.1 mm; acquisition time: 466 s; repetition time TR = 2300 ms; and echo time TE = 2.98 ms

References

Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 320 ch (n=22 recordings)

Sampling frequencies: 2000.0 Hz (n=22 recordings)

Total recording duration: 1 h 13 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 320 ch · MEG · 2000 Hz · 12 subjects, 33 recordings

Live trace viewer — sub-12 · task-localizer

Showing one representative recording out of 12 subjects and 33 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.DS003392(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Zilber2020

Canonical—

Importable asDS003392 · Zilber2020

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

class eegdash.dataset.DS003392(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

NeuroSpin hMT+ Localizer DATA (MEG & aMRI)

Study:: ds003392 (OpenNeuro)
Author (year):: Zilber2020
Canonical:: —

Also importable as: DS003392, Zilber2020.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 33; 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/ds003392 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003392 DOI: https://doi.org/10.18112/openneuro.ds003392.v1.0.4 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS003392
>>> dataset = DS003392(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/ds003392 · pull with datasets.load_dataset("EEGDash/ds003392").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS003392.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 ds003392 to reproduce the tutorial on this dataset.

Citation

Nicolas Zilber, Philippe Ciuciu, Alexandre Gramfort, Leila Azizi, Virginie van Wassenhove (2014). NeuroSpin hMT+ Localizer DATA (MEG & aMRI). 10.18112/openneuro.ds003392.v1.0.4

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds003392.v1.0.4.

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DS002001MEG · 11 subj DS004011MEG · 22 subj DS002312MEG · 19 subj DS000117MEG · 17 subj DS003352MEG · 18 subj

+ 1 more — see See Also below →

BIDS

BIDS ?

Sidecars

coordsystem

Provenance

CC0 · 10.18112/openneuro.ds003392.v1.0.4

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS003392`
Title	NeuroSpin hMT+ Localizer DATA (MEG & aMRI)
Author (year)	`Zilber2020`
Canonical	—
Importable as	`DS003392`, `Zilber2020`
Year	2014
Authors	Nicolas Zilber, Philippe Ciuciu, Alexandre Gramfort, Leila Azizi, Virginie van Wassenhove
License	CC0
Citation / DOI	10.18112/openneuro.ds003392.v1.0.4
Source links	OpenNeuro \| NeMAR \| Source URL

DS003392: meg dataset, 12 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#