DS004346#

FLUX: A pipeline for MEG analysis

Access recordings and metadata through EEGDash.

Citation: Oscar Ferrante, Ling Liu, Tamas Minarik, Urszula Gorska, Tara Ghafari, Huan Luo, Ole Jensen (2022). FLUX: A pipeline for MEG analysis. 10.18112/openneuro.ds004346.v1.0.8

Modality: meg Subjects: 1 Recordings: 19 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004346

dataset = DS004346(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 = DS004346(cache_dir="./data", subject="01")

Advanced query

dataset = DS004346(
    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{ds004346,
  title = {FLUX: A pipeline for MEG analysis},
  author = {Oscar Ferrante and Ling Liu and Tamas Minarik and Urszula Gorska and Tara Ghafari and Huan Luo and Ole Jensen},
  doi = {10.18112/openneuro.ds004346.v1.0.8},
  url = {https://doi.org/10.18112/openneuro.ds004346.v1.0.8},
}

About This Dataset#

References

Ferrante, O., Liu, L., Minarik, T., Gorska, U., Ghafari, T., Luo, H., & Jensen, O. (2022). FLUX: A pipeline for MEG analysis. NeuroImage, 253, 119047. https://doi.org/10.1016/j.neuroimage.2022.119047

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

Dataset Information#

Dataset ID	`DS004346`
Title	FLUX: A pipeline for MEG analysis
Year	2022
Authors	Oscar Ferrante, Ling Liu, Tamas Minarik, Urszula Gorska, Tara Ghafari, Huan Luo, Ole Jensen
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004346.v1.0.8
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: 1
Recordings: 19
Tasks: 1

Channels & sampling rate

Channels: 306 (2), 343 (2)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004346 class to access this dataset programmatically.

class eegdash.dataset.DS004346(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004346. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 3; 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/ds004346 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004346

Examples

>>> from eegdash.dataset import DS004346
>>> dataset = DS004346(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