EEGdash›OpenNeuro›DS006695

Iss. 6695 · 19 subjects · 19 recordings · CC0

Dataset Brief · Validation of Sleep Staging with Forehead EEG Patch

DS006695: eeg dataset, 19 subjects#

Validation of Sleep Staging with Forehead EEG Patch

Citation: Julie Onton, Sarah Mednick (20). Validation of Sleep Staging with Forehead EEG Patch. 10.18112/openneuro.ds006695.v1.0.2

19-participant EEG dataset — Validation of Sleep Staging with Forehead EEG Patch.

Data & curation Julie Onton · Sarah Mednick
Year 20 · Distributed via OpenNeuro

EEG · 3 ch500 HzBIDS 1.8.0Task · sleepHealthySleepSleep

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006695

dataset = DS006695(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 = DS006695(cache_dir="./data", subject="01")

Advanced query

dataset = DS006695(
    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{ds006695,
  title = {Validation of Sleep Staging with Forehead EEG Patch},
  author = {Julie Onton and Sarah Mednick},
  doi = {10.18112/openneuro.ds006695.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds006695.v1.0.2},
}

§ 02Study · The README

About This Dataset#

Curated EEG recordings for validating sleep staging from a three-electrode forehead patch against standard 33-channel polysomnography.

EEG.VisualHypnogram is manual scoring in 30-second epochs using the following integers

1 equals Wake 2 equals REM 3 equals N1 4 equals N2 5 equals N3 0 equals unknown or movement EEG.SpectralScoring is spectral staging from the forehead patch. One row per patch channel. One column per 30-second epoch (see publication).

UCSD Forehead Patch Sleep Validation Dataset

Alignment policy

The 33-channel cap data used to score polysomnography and the 3-channel patch EEG data do not always start and stop at the same clock times. CGX patch data were aligned to the cap start time based on a spreadsheet completed by the data collector, so the start may be off by a few seconds. The 3-channel EEG data were segmented into 30-second windows, and the number of these windows should approximately match the number of values in the EEG.VisualHypnogram for the same dataset. If the patch data ended up shorter than the visual hypnogram, the hypnogram was trimmed at the end to match the patch length. If the hypnogram was longer, it was left untrimmed. In general, the mismatch at the end of the recording is less than one 30-second window.

Subject exclusions

113 and 121 are excluded. The CGX patch was inadequate or unavailable.

Citation

Onton JA, Simon KC, Morehouse AB, Shuster AE, Zhang J, Peña AA, Mednick SC. Validation of spectral sleep scoring with polysomnography using forehead EEG device. Frontiers in Sleep. 2024. doi 10.3389/frsle.2024.1349537.

American Academy of Sleep Medicine. The AASM manual for the scoring of sleep and associated events. 2007 and later.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=19, range 19–29 yr, mean 22.9 yr)

152025

Female · 10Male · 9

Sex composition

Female
10
Male
9
F : M ratio
1.11 : 1

53% female · n = 19 subjects with reported sex.

Channel counts: 3 ch (n=19 recordings)

Sampling frequencies: 500.0 Hz (n=19 recordings)

Total recording duration: 164 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 3 ch · EEG · 500 Hz · 19 subjects, 19 recordings

Live trace viewer — sub-101 · task-sleep

Showing one representative recording out of 19 subjects and 19 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

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

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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS006695(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Onton2025

Canonical—

Importable asDS006695 · Onton2025

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS006695(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Validation of Sleep Staging with Forehead EEG Patch

Study:: ds006695 (OpenNeuro)
Author (year):: Onton2025
Canonical:: —

Also importable as: DS006695, Onton2025.

Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 19; recordings: 19; 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/ds006695 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006695 DOI: https://doi.org/10.18112/openneuro.ds006695.v1.0.2

Examples

>>> from eegdash.dataset import DS006695
>>> dataset = DS006695(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FacePre-bundled mirror at EEGDash/ds006695 · pull with datasets.load_dataset("EEGDash/ds006695").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006695.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 ds006695 to reproduce the tutorial on this dataset.

Citation

Julie Onton, Sarah Mednick (20). Validation of Sleep Staging with Forehead EEG Patch. 10.18112/openneuro.ds006695.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds006695.v1.0.2.

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+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds006695.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006695`
Title	Validation of Sleep Staging with Forehead EEG Patch
Author (year)	`Onton2025`
Canonical	—
Importable as	`DS006695`, `Onton2025`
Year	20
Authors	Julie Onton, Sarah Mednick
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006695.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS006695: eeg dataset, 19 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#