ON006695: eeg dataset, 19 subjects#
Validation of Sleep Staging with Forehead EEG Patch
Access recordings and metadata through EEGDash.
Citation: Julie Onton, Sarah Mednick (20). Validation of Sleep Staging with Forehead EEG Patch. 10.82901/nemar.on006695
Modality: eeg Subjects: 19 Recordings: 19 License: CC0 Source: nemar
Metadata: Good (80%)
19-participant EEG dataset — Validation of Sleep Staging with Forehead EEG Patch.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON006695
dataset = ON006695(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON006695(cache_dir="./data", subject="01")
Advanced query
dataset = ON006695(
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{on006695,
title = {Validation of Sleep Staging with Forehead EEG Patch},
author = {Julie Onton and Sarah Mednick},
doi = {10.82901/nemar.on006695},
url = {https://doi.org/10.82901/nemar.on006695},
}
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.
Cohort#
Dataset Statistics#
Age distribution (n=19, range 19–29 yr, mean 22.9 yr · sex per subject not reported)
Sex composition
Signal · Electrodes & live trace#
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
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.
Full dataset metadata table
Dataset ID |
|
Title |
Validation of Sleep Staging with Forehead EEG Patch |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Julie Onton, Sarah Mednick |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on006695,
title = {Validation of Sleep Staging with Forehead EEG Patch},
author = {Julie Onton and Sarah Mednick},
doi = {10.82901/nemar.on006695},
url = {https://doi.org/10.82901/nemar.on006695},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON006695(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Validation of Sleep Staging with Forehead EEG Patch
- Study:
on006695(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON006695.Modality:
eeg; Subject type:Unknown. 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/on006695 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on006695 DOI: https://doi.org/10.82901/nemar.on006695
Examples
>>> from eegdash.dataset import ON006695 >>> dataset = ON006695(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
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 on006695 to reproduce the tutorial on this dataset.
Citation
Julie Onton, Sarah Mednick (20). Validation of Sleep Staging with Forehead EEG Patch. 10.82901/nemar.on006695
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on006695.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset