DS007524: meg dataset, 50 subjects#

LittlePrince_MEG_French_Read_Pallier2025

Access recordings and metadata through EEGDash.

Citation: Corentin Bel, Julie Bonnaire, Jean-Rémi King, Christophe Pallier (2026). LittlePrince_MEG_French_Read_Pallier2025. 10.18112/openneuro.ds007524.v1.0.1

Modality: meg Subjects: 50 Recordings: 500 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007524

dataset = DS007524(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 = DS007524(cache_dir="./data", subject="01")

Advanced query

dataset = DS007524(
    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{ds007524,
  title = {LittlePrince_MEG_French_Read_Pallier2025},
  author = {Corentin Bel and Julie Bonnaire and Jean-Rémi King and Christophe Pallier},
  doi = {10.18112/openneuro.ds007524.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007524.v1.0.1},
}

About This Dataset#

Summary

This dataset contains magnetoencephalography (MEG) recordings collected while participants read the French text of Le Petit Prince presented using a rapid serial visual presentation (RSVP) paradigm. A separate dataset containing MEG recordings from the auditory listening paradigm is available on OpenNeuro (accession number: ds007523). This data is analyzed in: d’Ascoli, S., Bel, C., Rapin, J. et al. Towards decoding individual words from non-invasive brain recordings. Nature Communications 16, 10521 (2025). https://doi.org/10.1038/s41467-025-65499-0

Participants

View full README

Summary

This dataset contains magnetoencephalography (MEG) recordings collected while participants read the French text of Le Petit Prince presented using a rapid serial visual presentation (RSVP) paradigm. A separate dataset containing MEG recordings from the auditory listening paradigm is available on OpenNeuro (accession number: ds007523). This data is analyzed in: d’Ascoli, S., Bel, C., Rapin, J. et al. Towards decoding individual words from non-invasive brain recordings. Nature Communications 16, 10521 (2025). https://doi.org/10.1038/s41467-025-65499-0

Participants

Fifty healthy adults participated in the reading experiment (10 females; mean age = 28.4 years, SD = 5.7 years). All participants were native French speakers, right-handed, and reported no history of neurological disorders. Written informed consent was obtained prior to participation. The study was approved by the relevant local ethics committee.

Stimuli

The stimulus consisted of the French text of *Le Petit Prince*. The text was presented using a rapid serial visual presentation (RSVP) paradigm: - Words were displayed individually in white font on a black background - Word duration: 225 ms - Inter-word interval: 50 ms (black screen) - Sentence-final pause: 500 ms

Timing parameters were selected based on pilot testing to maintain attention and reading fluency. The text was segmented into 9 parts corresponding to the 9 experimental runs. - Mean run duration: 8min10s - SD: 40s

- Range: 7min10s to 9min

Experimental Procedure

After informed consent and familiarization with the MEG environment, participants were seated in the MEG chair inside a magnetically shielded room facing a projection screen. Viewing distance was fixed at 100 cm. Words appeared sequentially at the center of the screen. Participants were instructed to maintain fixation and read attentively while minimizing movement. The experiment consisted of 9 runs. Short breaks were provided between runs. After each run, participants completed 4 multiple-choice comprehension questions to assess engagement (behavioral responses are not included in this release).

Acquisition

MEG

MEG data for all three tasks were recorded inside the same magnetically shielded room using a whole-head Elekta Neuromag TRIUX MEG system (Elekta Oy, Helsinki, Finland), equipped with 102 magnetometers and 204 planar gradiometers. Data were recorded continuously with a sampling rate of 1000 Hz and an online low-pass filter at 330 Hz and high-pass filter at 0.1 Hz. Vertical and horizontal electrooculograms (EOG) and an electrocardiogram (ECG) were recorded simultaneously using bipolar electrodes to monitor eye movements and heartbeats.

Anatomical MRI

For each participant, a high-resolution T1-weighted anatomical MRI scan was acquired using a 3T Siemens Magnetom Prisma MRI scanner (Siemens Healthcare, Erlangen, Germany). A standard MPRAGE sequence was used. MRI scans were typically acquired right after the MEG recording. Scans were used for coregistration and cortical surface reconstruction for source analysis.

Data Organization

Raw Data

The root directory includes: - dataset_description.json - participants.tsv and participants.json - task-read_events.json - sub-01 to sub-50 - sourcedata/ - derivatives/

Each subject directory (sub-XX) contains one session (ses-01) with: - anat/: T1-weighted MRI (sub-XX_ses-01_T1w.nii.gz) and corresponding JSON sidecar - meg/: 9 MEG runs (task-read_run-01 to run-09), each including:

continuous MEG data (*_meg.fif)

sidecar JSON files

events.tsv and channels.tsv files

coordinate system file (*_coordsystem.json)

calibration and crosstalk files

sub-XX_ses-01_scans.tsv: scan-level metadata

Each run corresponds to one text segment. Acquisition parameters are provided in the corresponding sidecar JSON files.

Derivatives

The derivatives/ directory contains: - freesurfer/: subject-specific FreeSurfer reconstructions and morph maps - preprocessed_data/: preprocessed MEG data (including SSS-processed files), forward and inverse solutions, noise covariance matrices, source spaces, transformation files, evoked data, and source time courses.

Reference

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

Dataset Information#

Dataset ID	`DS007524`
Title	LittlePrince_MEG_French_Read_Pallier2025
Author (year)	`Pallier2025`
Canonical	—
Importable as	`DS007524`, `Pallier2025`
Year	2026
Authors	Corentin Bel, Julie Bonnaire, Jean-Rémi King, Christophe Pallier
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007524.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 50
Recordings: 500
Tasks: 1

Channels & sampling rate

Channels: 346 (414), 339 (27), 338 (9)
Sampling rate (Hz): 1000.0
Duration (hours): 63.99154166666667

Electrode Layout#

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

Dataset Statistics#

Age distribution (n=50, range 20–45 yr)

202530354045

Sex distribution

10

40

Female Male Total: 50

Channel counts (ch)

338339346

Sampling frequencies: 1000.0 Hz (n=450 recordings)

Total recording duration: 63 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS007524 class to access this dataset programmatically.

class eegdash.dataset.DS007524(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

LittlePrince_MEG_French_Read_Pallier2025

Study:: ds007524 (OpenNeuro)
Author (year):: Pallier2025
Canonical:: —

Also importable as: DS007524, Pallier2025.

Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 50; recordings: 500; 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/ds007524 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007524 DOI: https://doi.org/10.18112/openneuro.ds007524.v1.0.1

Examples

>>> from eegdash.dataset import DS007524
>>> dataset = DS007524(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)