EEGdash›OpenNeuro›DS003633

Iss. 3633 · 12 subjects · 96 recordings · CC0

Dataset Brief · ForrestGump-MEG

DS003633: meg dataset, 12 subjects#

ForrestGump-MEG

Citation: Xingyu Liu, Yuxuan Dai, Hailun Xie, Zonglei Zhen (20). ForrestGump-MEG. 10.18112/openneuro.ds003633.v1.0.3

12-participant MEG dataset — ForrestGump-MEG.

Data & curation Xingyu Liu · Yuxuan Dai · Hailun Xie · Zonglei Zhen
Year 20 · Distributed via OpenNeuro
Funding National Key R&D Program of China (Grant No. 2019YFA0709503) · National Natural Science Foundation of China (Grant No. 31771251)

MEG · 409 (89), 378 (7) ch600 Hz · mixedBIDS 1.4.02 tasks8 sessionsHealthyMultisensoryPerception

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 DS003633

dataset = DS003633(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 = DS003633(cache_dir="./data", subject="01")

Advanced query

dataset = DS003633(
    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{ds003633,
  title = {ForrestGump-MEG},
  author = {Xingyu Liu and Yuxuan Dai and Hailun Xie and Zonglei Zhen},
  doi = {10.18112/openneuro.ds003633.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds003633.v1.0.3},
}

§ 02Study · The README

About This Dataset#

For details please refer to our paper on https://www.biorxiv.org/content/10.1101/2021.06.04.446837v1.

This dataset contains MEG data recorded from 11 subjects while watching the 2h long Chinese-dubbed audio-visual movie ‘Forrest Gump’. The data were acquired with a 275-channel CTF MEG. Auxiliary data (T1w) as well as derivation data such as preprocessed data and MEG-MRI co-registration are also included. Pre-process procedure description The T1w images stored as NIFTI files were minimally-preprocessed using the anatomical preprocessing pipeline from fMRIPrep with default settings. MEG data were pre-processed using MNE following a three-step procedure: 1. bad channels were detected and removed. 2. a high-pass filter of 1 Hz was applied to remove possible slow drifts from the continuous MEG data. 3. artifacts removal was performed with ICA. Stimulus material The audio-visual stimulus materials were from the Chinese-dubbed ‘Forrest Gump’ DVD released in 2013 (ISBN: 978-7-7991-3934-0), which cannot be publicly released due to copyright restrictions. The stimulus materials are available upon reasonable request and on condition of a research-only data use agreement (correspondence with Xingyu Liu, liuxingyu987@foxmail.com). Dataset content overview the data were organized following the MEG-BIDS using MNE-BIDS toolbox. the pre-processed MEG data The preprocessed MEG recordings including the preprocessed MEG data, the event files, the ICA decomposition and label files and the MEG-MRI coordinate transformation file are hosted here.

The preprocessed MRI volume, reconstructed surface, and other associations including transformation files are hosted here

ForrestGump-MEG: A audio-visual movie watching MEG dataset

|---./derivatives/preproc_meg-mne_mri-fmriprep/sub-xx/ses-movie/anat/
  |---sub-xx_ses-movie_desc-preproc_T1w.nii.gz
  |---sub-xx_ses-movie_hemi-L_inflated.surf.gii
  |---sub-xx_ses-movie_hemi-L_midthickness.surf.gii
  |---sub-xx_ses-movie_hemi-L_pial.surf.gii
  |---sub-xx_ses-movie_hemi-L_smoothwm.surf.gii

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=11, range 19–25 yr, mean 22.0 yr)

152025

Female · 6Male · 5

Sex composition

Female
6
Male
5
F : M ratio
1.20 : 1

55% female · n = 11 subjects with reported sex.

HandednessRight · 11

Channel counts (ch)

378409

Sampling frequencies (Hz)

6001200

Total recording duration: 21 h 58 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 409 (89), 378 (7) ch · MEG · 600 Hz · mixed · 12 subjects, 96 recordings

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.DS003633(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Liu2021

Canonical—

Importable asDS003633 · Liu2021

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

class eegdash.dataset.DS003633(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

ForrestGump-MEG

Study:: ds003633 (OpenNeuro)
Author (year):: Liu2021
Canonical:: —

Also importable as: DS003633, Liu2021.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 96; tasks: 2.

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/ds003633 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003633 DOI: https://doi.org/10.18112/openneuro.ds003633.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003633
>>> dataset = DS003633(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/ds003633 · pull with datasets.load_dataset("EEGDash/ds003633").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS003633.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 ds003633 to reproduce the tutorial on this dataset.

Citation

Xingyu Liu, Yuxuan Dai, Hailun Xie, Zonglei Zhen (20). ForrestGump-MEG. 10.18112/openneuro.ds003633.v1.0.3

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds003633.v1.0.3.

Related & sibling datasets

DS003922MEG · 14 subj DS000248MEG · 2 subj DS006334MEG · 30 subj DS004107MEG · 9 subj DS005346MEG · 30 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.4.0

Sidecars

events · channels · coordsystem

Provenance

CC0 · 10.18112/openneuro.ds003633.v1.0.3

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS003633`
Title	ForrestGump-MEG
Author (year)	`Liu2021`
Canonical	—
Importable as	`DS003633`, `Liu2021`
Year	20
Authors	Xingyu Liu, Yuxuan Dai, Hailun Xie, Zonglei Zhen
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003633.v1.0.3
Source links	OpenNeuro \| NeMAR \| Source URL

DS003633: meg dataset, 12 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#