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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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	—
Importable as	`NM000253`, `Wang2024_et_al_Brain`
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

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), 136 (3), 190 (3), 156 (3), 166 (3), 218 (2), 248 (2), 108, 158
Sampling rate (Hz): 2048.0
Duration (hours): 1.8153209092881943

Electrode Layout#

Electrode layout — iEEG · 128 sensors — 128 channels

Dataset Statistics#

Channel counts (ch)

108136156158164166190218248

Sampling frequencies: 2048.0 Hz (n=26 recordings)

Total recording duration: 1 h 48 min

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Files: —

Size: —

Subjects: —

Click to load file structure…

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:: —

Also importable as: NM000253, Wang2024_et_al_Brain.

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: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)