NM000185: eeg dataset, 100 subjects#

Sleep-EDF Expanded: Whole-Night PSG Recordings

Access recordings and metadata through EEGDash.

Citation: Bob Kemp, Aeilko H. Zwinderman, Bert Tuk, Hilbert A.C. Kamphuisen, Josefien J.L. Oberye (2000). Sleep-EDF Expanded: Whole-Night PSG Recordings. 10.13026/C2X676

Modality: eeg Subjects: 100 Recordings: 197 License: ODbL v1.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000185

dataset = NM000185(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000185(cache_dir="./data", subject="01")

Advanced query

dataset = NM000185(
    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{nm000185,
  title = {Sleep-EDF Expanded: Whole-Night PSG Recordings},
  author = {Bob Kemp and Aeilko H. Zwinderman and Bert Tuk and Hilbert A.C. Kamphuisen and Josefien J.L. Oberye},
  doi = {10.13026/C2X676},
  url = {https://doi.org/10.13026/C2X676},
}

About This Dataset#

Sleep-EDF Expanded: Whole-Night PSG Recordings

197 whole-night PSG recordings from PhysioNet Sleep-EDF Expanded. - Cassette study: 78 healthy subjects, ambulatory 48h recordings - Telemetry study: 22 subjects, Temazepam drug study

Channels: EEG Fpz-Cz, EEG Pz-Oz (100 Hz), EOG horizontal, EMG submental (+ respiration, temperature in some recordings) Sleep staging: Expert-annotated 30-second epochs in _events.tsv files. Stages: Wake, N1, N2, N3 (combines original S3+S4 per AASM), REM, Unknown. Original Rechtschaffen & Kales stages preserved in ‘original_stage’ column. Reference: Kemp et al. (2000) IEEE TBME 47(9), 1185-1194. PhysioNet: https://physionet.org/content/sleep-edfx/1.0.0/

Dataset Information#

Dataset ID

NM000185

Title

Sleep-EDF Expanded: Whole-Night PSG Recordings

Author (year)

Kemp2000

Canonical

SleepEDF, SleepEDFExpanded

Importable as

NM000185, Kemp2000, SleepEDF, SleepEDFExpanded

Year

2000

Authors

Bob Kemp, Aeilko H. Zwinderman, Bert Tuk, Hilbert A.C. Kamphuisen, Josefien J.L. Oberye

License

ODbL v1.0

Citation / DOI

doi:10.13026/C2X676

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000185,
  title = {Sleep-EDF Expanded: Whole-Night PSG Recordings},
  author = {Bob Kemp and Aeilko H. Zwinderman and Bert Tuk and Hilbert A.C. Kamphuisen and Josefien J.L. Oberye},
  doi = {10.13026/C2X676},
  url = {https://doi.org/10.13026/C2X676},
}

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: 100

  • Recordings: 197

  • Tasks: 1

Channels & sampling rate

  • Channels: 7 (153), 5 (44)

  • Sampling rate (Hz): 100

  • Duration (hours): 3849.036111111111

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 8.1 GB

  • File count: 197

  • Format: BIDS

License & citation

  • License: ODbL v1.0

  • DOI: doi:10.13026/C2X676

Provenance

API Reference#

Use the NM000185 class to access this dataset programmatically.

class eegdash.dataset.NM000185(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Sleep-EDF Expanded: Whole-Night PSG Recordings

Study:

nm000185 (NeMAR)

Author (year):

Kemp2000

Canonical:

SleepEDF, SleepEDFExpanded

Also importable as: NM000185, Kemp2000, SleepEDF, SleepEDFExpanded.

Modality: eeg. Subjects: 100; recordings: 197; 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/nm000185 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000185 DOI: https://doi.org/10.13026/C2X676

Examples

>>> from eegdash.dataset import NM000185
>>> dataset = NM000185(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#