NM000253: ieeg dataset, 10 subjects#

Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli

Access recordings and metadata through EEGDash.

Citation: Christopher Wang, Adam Yaari, Aaditya K Singh, Vighnesh Subramaniam, Dana Rosenfarb, Jan DeWitt, Pranav Misra, Joseph R Madsen, Scellig Stone, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu (2019). Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli. 10.48550/arXiv.2411.08343

Modality: ieeg Subjects: 10 Recordings: 26 License: CC BY 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000253

dataset = NM000253(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000253(cache_dir="./data", subject="01")

Advanced query

dataset = NM000253(
    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{nm000253,
  title = {Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli},
  author = {Christopher Wang and Adam Yaari and Aaditya K Singh and Vighnesh Subramaniam and Dana Rosenfarb and Jan DeWitt and Pranav Misra and Joseph R Madsen and Scellig Stone and Gabriel Kreiman and Boris Katz and Ignacio Cases and Andrei Barbu},
  doi = {10.48550/arXiv.2411.08343},
  url = {https://doi.org/10.48550/arXiv.2411.08343},
}

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 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

NM000253

Title

Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli

Author (year)

Wang2024_et_al_Brain

Canonical

BrainTreeBank

Importable as

NM000253, Wang2024_et_al_Brain, BrainTreeBank

Year

2019

Authors

Christopher Wang, Adam Yaari, Aaditya K Singh, Vighnesh Subramaniam, Dana Rosenfarb, Jan DeWitt, Pranav Misra, Joseph R Madsen, Scellig Stone, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu

License

CC BY 4.0

Citation / DOI

doi:10.48550/arXiv.2411.08343

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000253,
  title = {Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli},
  author = {Christopher Wang and Adam Yaari and Aaditya K Singh and Vighnesh Subramaniam and Dana Rosenfarb and Jan DeWitt and Pranav Misra and Joseph R Madsen and Scellig Stone and Gabriel Kreiman and Boris Katz and Ignacio Cases and Andrei Barbu},
  doi = {10.48550/arXiv.2411.08343},
  url = {https://doi.org/10.48550/arXiv.2411.08343},
}

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: 10

  • Recordings: 26

  • Tasks: 1

Channels & sampling rate

  • Channels: 164 (8), 156 (3), 166 (3), 190 (3), 136 (3), 248 (2), 218 (2), 108, 158

  • Sampling rate (Hz): 2048

  • Duration (hours): 1.8153209092881943

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 257.3 GB

  • File count: 26

  • Format: BIDS

License & citation

  • License: CC BY 4.0

  • DOI: doi:10.48550/arXiv.2411.08343

Provenance

API Reference#

Use the NM000253 class to access this dataset programmatically.

class eegdash.dataset.NM000253(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli

Study:

nm000253 (NeMAR)

Author (year):

Wang2024_et_al_Brain

Canonical:

BrainTreeBank

Also importable as: NM000253, Wang2024_et_al_Brain, BrainTreeBank.

Modality: ieeg. Subjects: 10; recordings: 26; 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/nm000253 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000253 DOI: https://doi.org/10.48550/arXiv.2411.08343

Examples

>>> from eegdash.dataset import NM000253
>>> dataset = NM000253(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#