NM000181: eeg dataset, 2417 subjects#

NMT: Neurodiagnostic Montage Template Scalp EEG

Access recordings and metadata through EEGDash.

Citation: Hussain A. Khan (2019). NMT: Neurodiagnostic Montage Template Scalp EEG. 10.5281/zenodo.10909103

Modality: eeg Subjects: 2417 Recordings: 2417 License: CC BY-SA 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000181

dataset = NM000181(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000181(cache_dir="./data", subject="01")

Advanced query

dataset = NM000181(
    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{nm000181,
  title = {NMT: Neurodiagnostic Montage Template Scalp EEG},
  author = {Hussain A. Khan},
  doi = {10.5281/zenodo.10909103},
  url = {https://doi.org/10.5281/zenodo.10909103},
}

About This Dataset#

NMT: Neurodiagnostic Montage Template Scalp EEG Dataset

Overview

2,417 clinical EEG recordings (normal and abnormal) in standard 10-20 montage with 19 EEG channels + 2 reference electrodes. EDF format, variable sampling rates and durations. This dataset was collected for EEG-based pathology detection and normal/abnormal classification tasks. Source: Zenodo (doi:10.5281/zenodo.10909103) License: CC BY-SA 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 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

Dataset Information#

Dataset ID

NM000181

Title

NMT: Neurodiagnostic Montage Template Scalp EEG

Author (year)

Khan2019

Canonical

—

Importable as

NM000181, Khan2019

Year

2019

Authors

Hussain A. Khan

License

CC BY-SA 4.0

Citation / DOI

doi:10.5281/zenodo.10909103

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000181,
  title = {NMT: Neurodiagnostic Montage Template Scalp EEG},
  author = {Hussain A. Khan},
  doi = {10.5281/zenodo.10909103},
  url = {https://doi.org/10.5281/zenodo.10909103},
}

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: 2417

  • Recordings: 2417

  • Tasks: 1

Channels & sampling rate

  • Channels: 21

  • Sampling rate (Hz): 200

  • Duration (hours): 488.9631958333334

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 13.8 GB

  • File count: 2417

  • Format: BIDS

License & citation

  • License: CC BY-SA 4.0

  • DOI: doi:10.5281/zenodo.10909103

Provenance

API Reference#

Use the NM000181 class to access this dataset programmatically.

class eegdash.dataset.NM000181(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

NMT: Neurodiagnostic Montage Template Scalp EEG

Study:

nm000181 (NeMAR)

Author (year):

Khan2019

Canonical:

—

Also importable as: NM000181, Khan2019.

Modality: eeg. Subjects: 2417; recordings: 2417; 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/nm000181 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000181 DOI: https://doi.org/10.5281/zenodo.10909103

Examples

>>> from eegdash.dataset import NM000181
>>> dataset = NM000181(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#