EEGdash›OpenNeuro›DS007523

Iss. 7523 · 58 subjects · 579 recordings · CC0

Dataset Brief · LPP MEG Listen

DS007523: meg dataset, 58 subjects#

LPP MEG Listen

Citation: Corentin Bel, Julie Bonnaire, Christophe Pallier, Jean-Rémi King (2025). LPP MEG Listen. 10.18112/openneuro.ds007523.v1.0.1

58-participant MEG dataset — LPP MEG Listen.

Data & curation Corentin Bel · Julie Bonnaire · Christophe Pallier · Jean-Rémi King
Year 2025 · Distributed via OpenNeuro

MEG · 346 (484), 404 (9), 400 (9), 329 (9), 343 (9), 321 ch1000 HzBIDS 1.7.0Task · listenHealthyAuditoryPerception

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 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.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007523.v1.0.1},
}

§ 02Study · The README

About This Dataset#

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

Summary

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

View full README

Summary

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.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=58, range 18–43 yr, mean 27.8 yr)

1520253040

Female · 19Male · 39

Sex composition

Female
19
Male
39
F : M ratio
0.49 : 1

33% female · n = 58 subjects with reported sex.

HandednessRight · 58

Channel counts (ch)

321329343346400404

Sampling frequencies: 1000.0 Hz (n=521 recordings)

Total recording duration: 94 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 346 (484), 404 (9), 400 (9), 329 (9), 343 (9), 321 ch · MEG · 1000 Hz · 58 subjects, 579 recordings

Live trace viewer — sub-49 · ses-01 · task-listen · run-05

Showing one representative recording out of 58 subjects and 579 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.

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.DS007523(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Bel2026

Canonical—

Importable asDS007523 · Bel2026

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

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

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.v1.0.1

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)

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 — DS007523.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 ds007523 to reproduce the tutorial on this dataset.

Citation

Corentin Bel, Julie Bonnaire, Christophe Pallier, Jean-Rémi King (2025). LPP MEG Listen. 10.18112/openneuro.ds007523.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds007523.v1.0.1.

Related & sibling datasets

DS006468MEG · 24 subj DS003703MEG · 34 subj DS004276MEG · 19 subj DS006629MEG · 19 subj DS000246MEG · 2 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

coordsystem

Provenance

CC0 · 10.18112/openneuro.ds007523.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007523`
Title	LPP MEG Listen
Author (year)	`Bel2026`
Canonical	—
Importable as	`DS007523`, `Bel2026`
Year	2025
Authors	Corentin Bel, Julie Bonnaire, Christophe Pallier, Jean-Rémi King
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007523.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS007523: meg dataset, 58 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#