NM000253: ieeg dataset, 10 subjects#
Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli
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
10-participant iEEG dataset — Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli.
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#
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
References
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies: 2048.0 Hz (n=26 recordings)
Total recording duration: 1 h 48 min
Signal · Electrodes & live trace#
Live trace viewer — sub-08 · task-movie · run-01
Showing one representative recording out of
10 subjects and 26 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _ieeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?ieeg=<url>) to inspect it.
Electrode layout — iEEG · 128 sensors — 128 channels
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
Manifest#
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.
Full dataset metadata table
Dataset ID |
|
Title |
Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli |
Author (year) |
|
Canonical |
— |
Importable as |
|
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 |
|
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},
}
API Reference#
eegdash.datasetEEGDashDatasetNM000253 · Wang2024_et_al_Braineegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.NM000253(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for nm000253 to reproduce the tutorial on this dataset.
Citation
Christopher Wang, Adam Yaari, Aaditya K Singh, Vighnesh Subramaniam, Dana Rosenfarb, … (2019). Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli. 10.48550/arXiv.2411.08343
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.48550/arXiv.2411.08343.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset