DS004278#

Sustained Neural Representations of Personally Familiar People and Places During Cued Recall

Access recordings and metadata through EEGDash.

Citation: Alexis Kidder(*), Anna Corriveau(*), Lina Teichmann, Susan Wardle, Chris Baker, [(*) = equal contribution] (2022). Sustained Neural Representations of Personally Familiar People and Places During Cued Recall. 10.18112/openneuro.ds004278.v1.0.1

Modality: meg Subjects: 30 Recordings: 844 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004278

dataset = DS004278(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004278(cache_dir="./data", subject="01")

Advanced query

dataset = DS004278(
    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{ds004278,
  title = {Sustained Neural Representations of Personally Familiar People and Places During Cued Recall},
  author = {Alexis Kidder(*) and Anna Corriveau(*) and Lina Teichmann and Susan Wardle and Chris Baker and [(*) = equal contribution]},
  doi = {10.18112/openneuro.ds004278.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004278.v1.0.1},
}

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

DS004278

Title

Sustained Neural Representations of Personally Familiar People and Places During Cued Recall

Year

2022

Authors

Alexis Kidder(*), Anna Corriveau(*), Lina Teichmann, Susan Wardle, Chris Baker, [(*) = equal contribution]

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004278.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004278,
  title = {Sustained Neural Representations of Personally Familiar People and Places During Cued Recall},
  author = {Alexis Kidder(*) and Anna Corriveau(*) and Lina Teichmann and Susan Wardle and Chris Baker and [(*) = equal contribution]},
  doi = {10.18112/openneuro.ds004278.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004278.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: 30

  • Recordings: 844

  • Tasks: 1

Channels & sampling rate

  • Channels: 306

  • Sampling rate (Hz): 1200.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 76.7 GB

  • File count: 844

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004278.v1.0.1

Provenance

API Reference#

Use the DS004278 class to access this dataset programmatically.

class eegdash.dataset.DS004278(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004278. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 30; recordings: 30; 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/ds004278 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004278

Examples

>>> from eegdash.dataset import DS004278
>>> dataset = DS004278(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#