DS004738#

sfb_meg_phantom (B04/C01)

Access recordings and metadata through EEGDash.

Citation: Bahne H. Bahners, Roxanne Lofredi, Tilmann Sander, Alfons Schnitzler, Andrea A. Kuhn, Esther Florin (2023). sfb_meg_phantom (B04/C01). 10.18112/openneuro.ds004738.v1.0.1

Modality: meg Subjects: 4 Recordings: 99 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004738

dataset = DS004738(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004738(cache_dir="./data", subject="01")

Advanced query

dataset = DS004738(
    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{ds004738,
  title = {sfb_meg_phantom (B04/C01)},
  author = {Bahne H. Bahners and Roxanne Lofredi and Tilmann Sander and Alfons Schnitzler and Andrea A. Kuhn and Esther Florin},
  doi = {10.18112/openneuro.ds004738.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004738.v1.0.1},
}

About This Dataset#

This dataset is a part of the data used for the study: ‘Bahners B.H., Lofredi R. , Sander T., Schnitzler A., Kuhn A.A., Florin E. Deep brain stimulation device-specific artefacts in MEG recordings. 2023, submitted. doi: tba’

Please use the latest version of the dataset.

For detailed information about measurement protocol please refer to doi: tba. Additional information about Neuromag Phantom measurement is provided below.

Neuromag Phantom Measurement Movement onset is recorded with an accelerometer and captured with MISC006-MISC008

Dataset Information#

Dataset ID

DS004738

Title

sfb_meg_phantom (B04/C01)

Year

2023

Authors

Bahne H. Bahners, Roxanne Lofredi, Tilmann Sander, Alfons Schnitzler, Andrea A. Kuhn, Esther Florin

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004738.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004738,
  title = {sfb_meg_phantom (B04/C01)},
  author = {Bahne H. Bahners and Roxanne Lofredi and Tilmann Sander and Alfons Schnitzler and Andrea A. Kuhn and Esther Florin},
  doi = {10.18112/openneuro.ds004738.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004738.v1.0.1},
}

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: 4

  • Recordings: 99

  • Tasks: 2

Channels & sampling rate

  • Channels: 9 (13), 323 (13), 160 (12), 125 (12)

  • Sampling rate (Hz): 5000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Other

  • Modality: Other

  • Type: Other

Files & format

  • Size on disk: 6.1 GB

  • File count: 99

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004738.v1.0.1

Provenance

API Reference#

Use the DS004738 class to access this dataset programmatically.

class eegdash.dataset.DS004738(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004738. Modality: meg; Experiment type: Other; Subject type: Other. Subjects: 4; recordings: 25; 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/ds004738 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004738

Examples

>>> from eegdash.dataset import DS004738
>>> dataset = DS004738(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#