DS005261#

Gloups_MEG

Access recordings and metadata through EEGDash.

Citation: Snezana Todorovic, Elin Runnqvist, Valerie Chanoine, Jean-Michel Badier (2024). Gloups_MEG. 10.18112/openneuro.ds005261.v3.0.0

Modality: meg Subjects: 17 Recordings: 434 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005261

dataset = DS005261(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 = DS005261(cache_dir="./data", subject="01")

Advanced query

dataset = DS005261(
    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{ds005261,
  title = {Gloups_MEG},
  author = {Snezana Todorovic and Elin Runnqvist and Valerie Chanoine and Jean-Michel Badier},
  doi = {10.18112/openneuro.ds005261.v3.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005261.v3.0.0},
}

About This Dataset#

README

Seventeen adult participants completed a learning task and a resting-state condition during MEG recording (4D NeuroImaging system with 248 magnetometer channels).

Current dataset: OpenNeuro MEG Dataset ds005261 (Gloups_MEG, https://openneuro.org/datasets/ds005261/versions/2.0.0; see Todorović et al., in revision).

The same participants performed an identical learning task during fMRI scanning.

Related dataset: OpenNeuro fMRI Dataset ds004597 (Gloups, https://openneuro.org/datasets/ds004597/versions/2.0.0; see Todorović et al., 2023).

Note: Participant identifiers differ between the fMRI and MEG datasets. For details, refer to Table 1 in Todorović et al., in revision.

References 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

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

Todorović, S., Anton, J.-L., Sein, J., Nazarian, B., Chanoine, V., Rauchbauer, B., Kotz, S. A., & Runnqvist, E. (2023). Cortico-Cerebellar Monitoring of Speech Sequence Production. Neurobiology of Language, 1–21.

Todorović, S., Chanoine, V., Nazarian, B., Badier, J-M., Kanzari, K., Brovelli, A., Kotz, S. A., & Runnqvist, E. (in revision). Dataset for Evaluating the Production of Phonotactically Legal and Illegal Pseudowords. Scientific Data.

Dataset Information#

Dataset ID	`DS005261`
Title	Gloups_MEG
Year	2024
Authors	Snezana Todorovic, Elin Runnqvist, Valerie Chanoine, Jean-Michel Badier
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005261.v3.0.0
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: 17
Recordings: 434
Tasks: 2

Channels & sampling rate

Channels: 248 (137), 245 (46), 243 (36), 278 (31), 240 (2)
Sampling rate (Hz): 2034.5100996195154 (62), 2034.5101318359375 (14)
Duration (hours): 0.0

API Reference#

Use the DS005261 class to access this dataset programmatically.

class eegdash.dataset.DS005261(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005261. Modality: meg; Experiment type: Learning; Subject type: Healthy. Subjects: 17; recordings: 128; 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/ds005261 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005261

Examples

>>> from eegdash.dataset import DS005261
>>> dataset = DS005261(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