DS004473#

sEEG Forced Two-Choice Task

Access recordings and metadata through EEGDash.

Citation: Alexander P. Rockhill, Alessandra Mantovani, Brittany Stedelin, Admed M. Raslan, Nicole C. Swann (2023). sEEG Forced Two-Choice Task. 10.18112/openneuro.ds004473.v1.0.2

Modality: ieeg Subjects: 8 Recordings: 126 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004473

dataset = DS004473(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004473(cache_dir="./data", subject="01")

Advanced query

dataset = DS004473(
    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{ds004473,
  title = {sEEG Forced Two-Choice Task},
  author = {Alexander P. Rockhill and Alessandra Mantovani and Brittany Stedelin and Admed M. Raslan and Nicole C. Swann},
  doi = {10.18112/openneuro.ds004473.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004473.v1.0.2},
}

About This Dataset#

Welcome to our dataset! Here we present stereoelectroencephalography data from a forced two-choice response task collected in the epilepsy monitoring unit at Oregon Health & Science University. The data was analyzed in collaboration with the University of Oregon. The accompanying paper the first reference below.

References

Rockhill, A. P., Mantovani, A., Stedelin, B., Nerison, C. S., Raslan, A. M., & Swann, N. C. (2022). Stereo-EEG recordings extend known distributions of canonical movement-related oscillations. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/acae0a

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

Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D’Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7

Dataset Information#

Dataset ID

DS004473

Title

sEEG Forced Two-Choice Task

Year

2023

Authors

Alexander P. Rockhill, Alessandra Mantovani, Brittany Stedelin, Admed M. Raslan, Nicole C. Swann

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004473.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004473,
  title = {sEEG Forced Two-Choice Task},
  author = {Alexander P. Rockhill and Alessandra Mantovani and Brittany Stedelin and Admed M. Raslan and Nicole C. Swann},
  doi = {10.18112/openneuro.ds004473.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004473.v1.0.2},
}

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: 8

  • Recordings: 126

  • Tasks: 1

Channels & sampling rate

  • Channels: 129

  • Sampling rate (Hz): 999.4121105232217

  • Duration (hours): 0.0

Tags

  • Pathology: Epilepsy

  • Modality: —

  • Type: Motor

Files & format

  • Size on disk: 6.3 GB

  • File count: 126

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004473.v1.0.2

Provenance

API Reference#

Use the DS004473 class to access this dataset programmatically.

class eegdash.dataset.DS004473(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004473. Modality: ieeg; Experiment type: Motor; Subject type: Epilepsy. Subjects: 8; recordings: 8; 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/ds004473 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004473

Examples

>>> from eegdash.dataset import DS004473
>>> dataset = DS004473(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#