DS004012#

BRAR_NQ

Access recordings and metadata through EEGDash.

Citation: Nur Syairah Ab Rani, Nurfaizatul Aisyah Ab Aziz, Mohammed Farouq Reza, Muzaimi Mustapha (2022). BRAR_NQ. 10.18112/openneuro.ds004012.v1.0.0

Modality: meg Subjects: 30 Recordings: 949 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004012

dataset = DS004012(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 = DS004012(cache_dir="./data", subject="01")

Advanced query

dataset = DS004012(
    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{ds004012,
  title = {BRAR_NQ},
  author = {Nur Syairah Ab Rani and Nurfaizatul Aisyah Ab Aziz and Mohammed Farouq Reza and Muzaimi Mustapha},
  doi = {10.18112/openneuro.ds004012.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004012.v1.0.0},
}

About This Dataset#

References

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	`DS004012`
Title	BRAR_NQ
Year	2022
Authors	Nur Syairah Ab Rani, Nurfaizatul Aisyah Ab Aziz, Mohammed Farouq Reza, Muzaimi Mustapha
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004012.v1.0.0
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: 30
Recordings: 949
Tasks: 10

Channels & sampling rate

Channels: 61 (294), 383 (294)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004012 class to access this dataset programmatically.

class eegdash.dataset.DS004012(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004012. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 30; recordings: 294; tasks: 10.

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/ds004012 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004012

Examples

>>> from eegdash.dataset import DS004012
>>> dataset = DS004012(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