NM000241: ieeg dataset, 2 subjects#

CerebroVoice: Bilingual sEEG Speech Dataset

Access recordings and metadata through EEGDash.

Citation: Xueyi Zhang (2019). CerebroVoice: Bilingual sEEG Speech Dataset. 10.5281/zenodo.13332808

Modality: ieeg Subjects: 2 Recordings: 18 License: CC BY 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000241

dataset = NM000241(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000241(cache_dir="./data", subject="01")

Advanced query

dataset = NM000241(
    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{nm000241,
  title = {CerebroVoice: Bilingual sEEG Speech Dataset},
  author = {Xueyi Zhang},
  doi = {10.5281/zenodo.13332808},
  url = {https://doi.org/10.5281/zenodo.13332808},
}

About This Dataset#

CerebroVoice: Bilingual sEEG Speech Dataset

Overview

Intracranial EEG (sEEG) recordings from 2 epilepsy patients during bilingual speech tasks (Mandarin Chinese, English, and digit reading). Recorded at 1000 Hz with Nihon Kohden EEG-1200, depth electrodes (platinum-iridium). Data distributed as preprocessed NPY derivatives: - LFS: Low-frequency signal - HGA: High-gamma activity - BBS: Broadband signal

Tasks: Chinese reading, English reading, digit reading Subjects: SUB1 (114 channels post-filtering), SUB2 (158 channels) Duration: ~73 min (SUB1), ~76 min (SUB2) Source: Zenodo (doi:10.5281/zenodo.13332808) License: CC BY 4.0

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

NM000241

Title

CerebroVoice: Bilingual sEEG Speech Dataset

Author (year)

Zhang2019

Canonical

—

Importable as

NM000241, Zhang2019

Year

2019

Authors

Xueyi Zhang

License

CC BY 4.0

Citation / DOI

doi:10.5281/zenodo.13332808

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000241,
  title = {CerebroVoice: Bilingual sEEG Speech Dataset},
  author = {Xueyi Zhang},
  doi = {10.5281/zenodo.13332808},
  url = {https://doi.org/10.5281/zenodo.13332808},
}

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: 2

  • Recordings: 18

  • Tasks: 9

Channels & sampling rate

  • Channels: 158 (6), 114 (6), 228 (3), 316 (3)

  • Sampling rate (Hz): 200

  • Duration (hours): 3.836216666666667

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 1.9 GB

  • File count: 18

  • Format: BIDS

License & citation

  • License: CC BY 4.0

  • DOI: doi:10.5281/zenodo.13332808

Provenance

API Reference#

Use the NM000241 class to access this dataset programmatically.

class eegdash.dataset.NM000241(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

CerebroVoice: Bilingual sEEG Speech Dataset

Study:

nm000241 (NeMAR)

Author (year):

Zhang2019

Canonical:

—

Also importable as: NM000241, Zhang2019.

Modality: ieeg. Subjects: 2; recordings: 18; tasks: 9.

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/nm000241 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000241 DOI: https://doi.org/10.5281/zenodo.13332808

Examples

>>> from eegdash.dataset import NM000241
>>> dataset = NM000241(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#