EEGdash›OpenNeuro›DS005345

Iss. 5345 · 26 subjects · 26 recordings · CC0

Dataset Brief · Le Petit Prince (LPP) Multi-talker

DS005345: eeg dataset, 26 subjects#

Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset

Citation: Zhengwu Ma, Nan Wang, Jixing Li (20). Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset. 10.18112/openneuro.ds005345.v1.0.1

26-participant EEG dataset — Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset.

Data & curation Zhengwu Ma · Nan Wang · Jixing Li
Year 20 · Distributed via OpenNeuro

EEG · 64 ch500 HzBIDS 1.8.0Task · multitalkerHealthyAuditoryAttention

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 DS005345

dataset = DS005345(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 = DS005345(cache_dir="./data", subject="01")

Advanced query

dataset = DS005345(
    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{ds005345,
  title = {Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset},
  author = {Zhengwu Ma and Nan Wang and Jixing Li},
  doi = {10.18112/openneuro.ds005345.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005345.v1.0.1},
}

§ 02Study · The README

About This Dataset#

This dataset includes 25 native Mandarin Chinese speakers (14 females, mean age = 24.04 ± 2.28 years) who participated in both EEG and fMRI experiments. The participants were all right-handed, with no reported history of neurological disorders. They were enrolled in undergraduate or graduate programs in Shanghai. All participants gave informed consent, and the experiments were approved by the Ethics Committee of the Ninth People’s Hospital, affiliated with Shanghai Jiao Tong University School of Medicine (SH9H-2019-T33-2 and SH9H-2022-T379-2).

In the case of French participants, due to legal constraints, additional session considerations were taken into account, such as shorter session durations.

Participants

Experiment Procedure

MRI Scanning Sessions Participants underwent both EEG and fMRI experiments while listening to the Chinese version of *Le Petit Prince*. During the MRI session, participants were instructed to maintain fixation on a crosshair on the screen and minimize eye movements and head motions. The task involved attending to different talkers in the multitalker condition (single male, single female, mixed male, and mixed female talkers).

Session Breakdown - The entire session lasted approximately 70 minutes for fMRI participants, including a series of 4 conditions (single-talker, mixed-attended, and mixed-unattended conditions). - Quiz questions were administered after each run to assess participants’ comprehension of the narrative.

View full README

Participants

Experiment Procedure

MRI Scanning Sessions Participants underwent both EEG and fMRI experiments while listening to the Chinese version of *Le Petit Prince*. During the MRI session, participants were instructed to maintain fixation on a crosshair on the screen and minimize eye movements and head motions. The task involved attending to different talkers in the multitalker condition (single male, single female, mixed male, and mixed female talkers).

Session Breakdown - The entire session lasted approximately 70 minutes for fMRI participants, including a series of 4 conditions (single-talker, mixed-attended, and mixed-unattended conditions). - Quiz questions were administered after each run to assess participants’ comprehension of the narrative.

In the French cohort, due to legal time constraints, the experiment durations were adjusted.

Stimuli

The stimuli were selected excerpts from the Chinese version of Le Petit Prince (available at xiaowangzi.org). These audio clips were previously used in both EEG (Li et al., 2024) and fMRI (Li et al., 2022) studies.

The English and Chinese versions were enhanced with visual stimuli (e.g., images of scenes from the book) to align with the storyline. However, visual stimuli were not presented in the French version to comply with legal restrictions.

Acquisition

MRI Hardware & Scanning Parameters - EEG: Data were collected using a 64-channel actiCAP system, sampled at 500 Hz, and filtered between 0.016 and 80 Hz. - fMRI: Scanning was performed on a 7.0 T Terra Siemens MRI scanner at the Zhangjiang International Brain Imaging Centre. The scanning parameters differed slightly between the English/Chinese and French studies due to equipment availability.

Functional MRI: 85 interleaved axial slices (1.6×1.6×1.6 mm voxel size, TR = 1000 ms, TE = 22.2 ms)

Anatomical MRI: MP-RAGE sequence, T1-weighted images (voxel size = 0.7×0.7×0.7 mm).

Preprocessing

MRI Data Processing 1. DICOM to NIfTI Conversion: All raw MRI data were converted to NIfTI format using dcm2niix (version 1.0.20220505) and processed using the fMRIPrep pipeline (version 20.2.0). 2. Anatomical Preprocessing:

Skull stripping

Segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF)

Registration to the Montreal Neurological Institute (MNI) space using MNI152NLin2009cAsym:res-2 template.

Functional Preprocessing: - Motion correction - Slice-timing correction - Multi-echo ICA for denoising - Voxel resampling to native and MNI spaces.

Note: Visual stimuli processing for the English and Chinese conditions was handled separately to avoid potential biases in the analysis.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=26, range 20–28 yr, mean 24.0 yr)

2025

Female · 15Male · 11

Sex composition

Female
15
Male
11
F : M ratio
1.36 : 1

58% female · n = 26 subjects with reported sex.

Channel counts: 64 ch (n=26 recordings)

Sampling frequencies: 500.0 Hz (n=26 recordings)

Total recording duration: 19 h 4 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 500 Hz · 26 subjects, 26 recordings

Live trace viewer — sub-13 · task-multitalker

Showing one representative recording out of 26 subjects and 26 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<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.DS005345(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Ma2024

Canonical—

Importable asDS005345 · Ma2024

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

class eegdash.dataset.DS005345(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset

Study:: ds005345 (OpenNeuro)
Author (year):: Ma2024
Canonical:: —

Also importable as: DS005345, Ma2024.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 26; recordings: 26; 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/ds005345 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005345 DOI: https://doi.org/10.18112/openneuro.ds005345.v1.0.1

Examples

>>> from eegdash.dataset import DS005345
>>> dataset = DS005345(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/ds005345 · pull with datasets.load_dataset("EEGDash/ds005345").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS005345.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 ds005345 to reproduce the tutorial on this dataset.

Citation

Zhengwu Ma, Nan Wang, Jixing Li (20). Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset. 10.18112/openneuro.ds005345.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.ds005345.v1.0.1.

Related & sibling datasets

DS004033EEG · 18 subj DS004940EEG · 22 subj DS003061EEG · 13 subj DS003620EEG · 44 subj DS007322EEG · 57 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

eeg.json

Provenance

CC0 · 10.18112/openneuro.ds005345.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS005345`
Title	Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset
Author (year)	`Ma2024`
Canonical	—
Importable as	`DS005345`, `Ma2024`
Year	20
Authors	Zhengwu Ma, Nan Wang, Jixing Li
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005345.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS005345: eeg dataset, 26 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#