DS004107: meg dataset, 9 subjects#

MIND DATA

Access recordings and metadata through EEGDash.

Citation: M.P. Weisend, F.M. Hanlon, R. Montano, S.P. Ahlfors, A.C. Leuthold, D. Pantazis, J.C. Mosher, A.P. Georgopoulos, M.S. Hamalainen, C.J. Aine (2022). MIND DATA. 10.18112/openneuro.ds004107.v1.0.0

Modality: meg Subjects: 9 Recordings: 89 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004107

dataset = DS004107(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004107(cache_dir="./data", subject="01")

Advanced query

dataset = DS004107(
    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{ds004107,
  title = {MIND DATA},
  author = {M.P. Weisend and F.M. Hanlon and R. Montano and S.P. Ahlfors and A.C. Leuthold and D. Pantazis and J.C. Mosher and A.P. Georgopoulos and M.S. Hamalainen and C.J. Aine},
  doi = {10.18112/openneuro.ds004107.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004107.v1.0.0},
}

About This Dataset#

This data was part of the study of: M.P. Weisend, F.M. Hanlon, R. Montaño, S.P. Ahlfors, A.C. Leuthold, D. Pantazis, J.C. Mosher, A.P. Georgopoulos, M.S. Hämäläinen, C.J. Aine,, V. (2007). Paving the way for cross-site pooling of magnetoencephalography (MEG) data. International Congress Series, Volume 1300, Pages 615-618,. It was converted to BIDS with MNE-BIDS: Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Following the MEG-BIDS format: 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

Dataset Information#

Dataset ID

DS004107

Title

MIND DATA

Author (year)

Weisend2022

Canonical

Weisend2007

Importable as

DS004107, Weisend2022, Weisend2007

Year

2022

Authors

M.P. Weisend, F.M. Hanlon, R. Montano, S.P. Ahlfors, A.C. Leuthold, D. Pantazis, J.C. Mosher, A.P. Georgopoulos, M.S. Hamalainen, C.J. Aine

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004107.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004107,
  title = {MIND DATA},
  author = {M.P. Weisend and F.M. Hanlon and R. Montano and S.P. Ahlfors and A.C. Leuthold and D. Pantazis and J.C. Mosher and A.P. Georgopoulos and M.S. Hamalainen and C.J. Aine},
  doi = {10.18112/openneuro.ds004107.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004107.v1.0.0},
}

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: 9

  • Recordings: 89

  • Tasks: 6

Channels & sampling rate

  • Channels: 318 (84), 317 (5)

  • Sampling rate (Hz): 1792.8858642578125 (57), 1250.0 (32)

  • Duration (hours): 23.117616255933115

Tags

  • Pathology: Healthy

  • Modality: Multisensory

  • Type: Other

Files & format

  • Size on disk: 77.2 GB

  • File count: 89

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004107.v1.0.0

Provenance

API Reference#

Use the DS004107 class to access this dataset programmatically.

class eegdash.dataset.DS004107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

MIND DATA

Study:

ds004107 (OpenNeuro)

Author (year):

Weisend2022

Canonical:

Weisend2007

Also importable as: DS004107, Weisend2022, Weisend2007.

Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 9; recordings: 89; tasks: 6.

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/ds004107 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004107 DOI: https://doi.org/10.18112/openneuro.ds004107.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004107
>>> dataset = DS004107(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#