DS005345#

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

Access recordings and metadata through EEGDash.

Citation: Zhengwu Ma, Nan Wang, Jixing Li (2024). Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset. 10.18112/openneuro.ds005345.v1.0.1

Modality: eeg Subjects: 26 Recordings: 421 License: CC0 Source: openneuro

Metadata: Complete (100%)

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},
}

About This Dataset#

Participants

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.

Experiment Procedure

View full README

Participants

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.

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

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).
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.

Dataset Information#

Dataset ID	`DS005345`
Title	Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset
Year	2024
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

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: 26
Recordings: 421
Tasks: 2

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS005345 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds005345. 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None