NM000185: eeg dataset, 100 subjects#

Name: Sleep-EDF Expanded: Whole-Night PSG Recordings
Published: 2000-01-01
License: ODbL v1.0

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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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	—
Importable as	`NM000185`, `Kemp2000`
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

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): 100.0
Duration (hours): 3849.036111111111

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Age distribution (n=100, range 18–101 yr)

152025303545505560657075859095100

Sex distribution

48

52

Female Male Total: 100

Channel counts (ch)

57

Sampling frequencies: 100.0 Hz (n=197 recordings)

Total recording duration: 3849 h

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 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:: —

Also importable as: NM000185, Kemp2000.

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: 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)