DS003633#

ForrestGump-MEG

Access recordings and metadata through EEGDash.

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

Modality: meg Subjects: 12 Recordings: 96 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

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

View full README

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

|---./derivatives/preproc_meg-mne_mri-fmriprep/sub-xx/ses-movie/meg/
        |---sub-xx_ses-movie_coordsystem.json
        |---sub-xx_ses-movie_task-movie_run-xx_channels.tsv
        |---sub-xx_ses-movie_task-movie_run-xx_decomposition.tsv
        |---sub-xx_ses-movie_task-movie_run-xx_events.tsv
        |---sub-xx_ses-movie_task-movie_run-xx_ica.fif.gz
        |---sub-xx_ses-movie_task-movie_run-xx_meg.fif
        |---sub-xx_ses-movie_task-movie_run-xx_meg.json
        |---...
        |---sub-xx_ses-movie_task-movie_trans.fif

the pre-processed MRI data The preprocessed MRI volume, reconstructed surface, and other associations including transformation files are hosted here

|---./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
        |---sub-xx_ses-movie_hemi-R_inflated.surf.gii
        |---sub-xx_ses-movie_hemi-R_midthickness.surf.gii
        |---sub-xx_ses-movie_hemi-R_pial.surf.gii
        |---sub-xx_ses-movie_hemi-R_smoothwm.surf.gii
        |---sub-xx_ses-movie_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz
        |---sub-xx_ses-movie_space-MNI152NLin6Asym_desc-preproc_T1w.nii.gz
        |---...

the FreeSurfer surface data, the high-resolution head surface and the MRI-fiducials are provided here

|---./derivatives/preproc_meg-mne_mri-fmriprep/sourcedata/
        |---freesurfer
            |---sub-xx
            |---...

the raw data

|---./sub-xx/ses-movie/
        |---meg/
        |       |---sub-xx_ses-movie_coordsystem.json
        |       |---sub-xx_ses-movie_task-movie_run-xx_channels.tsv
        |       |---sub-xx_ses-movie_task-movie_run-xx_events.tsv
        |       |---sub-xx_ses-movie_task-movie_run-xx_meg.ds
        |       |---sub-xx_ses-movie_task-movie_run-xx_meg.json
        |       |---...
        |---anat/
                |---sub-xx_ses-movie_T1w.json
                |---sub-xx_ses-movie_T1w.nii.gz

Dataset Information#

Dataset ID

DS003633

Title

ForrestGump-MEG

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
Copy-paste 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},
}

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: 12

  • Recordings: 96

  • Tasks: 2

Channels & sampling rate

  • Channels: 409 (89), 378 (7)

  • Sampling rate (Hz): 600.0 (89), 1200.0 (7)

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Multisensory

  • Type: Perception

Files & format

  • Size on disk: 154.6 GB

  • File count: 96

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds003633.v1.0.3

Provenance

API Reference#

Use the DS003633 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds003633. 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

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, 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

See Also#