DS007523: meg dataset, 58 subjects#

LPP MEG Listen

Access recordings and metadata through EEGDash.

Citation: Corentin Bel, Julie Bonnaire, Christophe Pallier, Jean-Rémi King (2026). LPP MEG Listen. 10.18112/openneuro.ds007523.v1.0.0

Modality: meg Subjects: 58 Recordings: 579 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007523

dataset = DS007523(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 = DS007523(cache_dir="./data", subject="01")

Advanced query

dataset = DS007523(
    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{ds007523,
  title = {LPP MEG Listen},
  author = {Corentin Bel and Julie Bonnaire and Christophe Pallier and Jean-Rémi King},
  doi = {10.18112/openneuro.ds007523.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007523.v1.0.0},
}

About This Dataset#

Summary

This dataset contains magnetoencephalography (MEG) recordings collected while participants listened to the French audiobook of Le Petit Prince by Antoine de Saint-Exupéry. A complementary MEG dataset from the same project, using a reading (RSVP) paradigm, is available on OpenNeuro (accession number: ds007524). 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

View full README

Summary

This dataset contains magnetoencephalography (MEG) recordings collected while participants listened to the French audiobook of Le Petit Prince by Antoine de Saint-Exupéry. A complementary MEG dataset from the same project, using a reading (RSVP) paradigm, is available on OpenNeuro (accession number: ds007524). 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-eight healthy adults participated in the listening experiment (17 females; mean age = 27.8 years, SD = 5.5 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 auditory stimulus consisted of the French audiobook version of *Le Petit Prince*. - Language: French - Format: Continuous audiobook - Segmentation: 9 parts - Mean duration per part: 10min50s - Standard deviation: 55s - Minimum duration: 9min40s - Maximum duration: 12min30s

The same audiobook version was previously used in a publicly available

fMRI dataset (Li et al., 2022).

Experimental Procedure

Participants were seated in the MEG system after informed consent and familiarization with the recording environment. Auditory stimuli were delivered through MEG-compatible earphones. Sound intensity was individually adjusted to a comfortable listening level before the experiment. Participants were instructed to listen attentively and remain as still as possible. The experiment consisted of 9 runs, corresponding to the 9 audiobook segments. Between runs, participants completed 4 multiple-choice comprehension questions presented visually on a screen (not reported here). Short breaks were provided between runs. Alertness and movement were monitored

via camera during recording.

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-listen_events.json - sub-01 to sub-58 - sourcedata/

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-listen_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 audiobook segment. Acquisition parameters are provided in the corresponding sidecar JSON

files.

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 Li, Jixing, et al. “Le Petit Prince Multilingual Naturalistic fMRI Corpus.” Scientific Data, vol. 9, no. 1, Aug. 2022, p. 530. www.nature.com, https://doi.org/10.1038/s41597-022-01625-7.

Dataset Information#

Dataset ID	`DS007523`
Title	LPP MEG Listen
Author (year)	`Bel2026`
Canonical	—
Importable as	`DS007523`, `Bel2026`
Year	2026
Authors	Corentin Bel, Julie Bonnaire, Christophe Pallier, Jean-Rémi King
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007523.v1.0.0
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: 58
Recordings: 579
Tasks: 1

Channels & sampling rate

Channels: 346 (484), 404 (9), 400 (9), 329 (9), 343 (9), 321
Sampling rate (Hz): 1000.0
Duration (hours): 94.80763305555556

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=58, range 18–43 yr)

1520253040

Sex distribution

19

39

Female Male Total: 58

Channel counts (ch)

321329343346400404

Sampling frequencies: 1000.0 Hz (n=521 recordings)

Total recording duration: 94 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 DS007523 class to access this dataset programmatically.

class eegdash.dataset.DS007523(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

LPP MEG Listen

Study:: ds007523 (OpenNeuro)
Author (year):: Bel2026
Canonical:: —

Also importable as: DS007523, Bel2026.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 58; recordings: 579; 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/ds007523 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007523 DOI: https://doi.org/10.18112/openneuro.ds007523.v1.0.0

Examples

>>> from eegdash.dataset import DS007523
>>> dataset = DS007523(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)