DS004541: eeg, fnirs dataset, 8 subjects#

Multimodal EEG-fNIRS data from patients undergoing general anesthesia

Access recordings and metadata through EEGDash.

Citation: Catalina Saini Ferrón, Gabriela Vargas González, Carlos Valle Araya (2023). Multimodal EEG-fNIRS data from patients undergoing general anesthesia. 10.18112/openneuro.ds004541.v1.0.0

Modality: eeg, fnirs Subjects: 8 Recordings: 18 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004541

dataset = DS004541(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004541(cache_dir="./data", subject="01")

Advanced query

dataset = DS004541(
    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{ds004541,
  title = {Multimodal EEG-fNIRS data from patients undergoing general anesthesia},
  author = {Catalina Saini Ferrón and Gabriela Vargas González and Carlos Valle Araya},
  doi = {10.18112/openneuro.ds004541.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004541.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 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

References

In preperation

Dataset Information#

Dataset ID

DS004541

Title

Multimodal EEG-fNIRS data from patients undergoing general anesthesia

Author (year)

Ferron2023

Canonical

Ferron2019

Importable as

DS004541, Ferron2023, Ferron2019

Year

2023

Authors

Catalina Saini Ferrón, Gabriela Vargas González, Carlos Valle Araya

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004541.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004541,
  title = {Multimodal EEG-fNIRS data from patients undergoing general anesthesia},
  author = {Catalina Saini Ferrón and Gabriela Vargas González and Carlos Valle Araya},
  doi = {10.18112/openneuro.ds004541.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004541.v1.0.0},
}

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: 18

  • Tasks: 1

Channels & sampling rate

  • Channels: 59 (9), 40 (5), 30 (3), 38

  • Sampling rate (Hz): 1000.0 (9), 7.8125 (9)

  • Duration (hours): 12.130006388888887

Tags

  • Pathology: Surgery

  • Modality: Anesthesia

  • Type: Clinical/Intervention

Files & format

  • Size on disk: 2.9 GB

  • File count: 18

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004541.v1.0.0

Provenance

API Reference#

Use the DS004541 class to access this dataset programmatically.

class eegdash.dataset.DS004541(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Multimodal EEG-fNIRS data from patients undergoing general anesthesia

Study:

ds004541 (OpenNeuro)

Author (year):

Ferron2023

Canonical:

Ferron2019

Also importable as: DS004541, Ferron2023, Ferron2019.

Modality: eeg, fnirs; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 8; recordings: 18; 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/ds004541 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004541 DOI: https://doi.org/10.18112/openneuro.ds004541.v1.0.0

Examples

>>> from eegdash.dataset import DS004541
>>> dataset = DS004541(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#