DS005207: eeg dataset, 20 subjects#
Surrey cEEGrid sleep data set
Citation: Kaare B. Mikkelsen, James K Ebajemito, Maria A Bonmati-Carrion, Nayantara Santhi, Victoria L Revell, Giuseppe Atzori, Laura Birch, Ciro Della Monica, Stefan Debener, Derk-Jan Dijk, Annette Sterr, Maarten De Vos (20). Surrey cEEGrid sleep data set. 10.18112/openneuro.ds005207.v1.0.0
20-participant EEG dataset — Surrey cEEGrid sleep data set.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005207
dataset = DS005207(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005207(cache_dir="./data", subject="01")
Advanced query
dataset = DS005207(
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{ds005207,
title = {Surrey cEEGrid sleep data set},
author = {Kaare B. Mikkelsen and James K Ebajemito and Maria A Bonmati-Carrion and Nayantara Santhi and Victoria L Revell and Giuseppe Atzori and Laura Birch and Ciro Della Monica and Stefan Debener and Derk-Jan Dijk and Annette Sterr and Maarten De Vos},
doi = {10.18112/openneuro.ds005207.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005207.v1.0.0},
}
About This Dataset#
Surrey sleep data set
Overview This dataset was collected as part of a research project on wearable sleep monitoring which took place in spring 2017.
The data set contains nightly EEG recordings from 20 healthy participants (‘subjects’). Some recordings are full polysomnography (PSG) measurements, others are cEEGrid measurements. Most subjects have both PSG and ceegrid recordings from the same night, though a few are missing one or the other.
Format The dataset is formatted according to the Brain Imaging Data Structure. See the ‘dataset_description.json’ file for the specific BIDS version used. The EEG data format chosen is the ‘.set’ format of EEGLAB.
For more information, see the following link: https://bids-specification.readthedocs.io/en/stable/01-introduction.html Task description The patient performed no tasks. The recording equipment was mounted immediately prior to bedtime, and the recordings took place at the sleep laboratory of the Surrey Clinical Research Centre.
Note that due to a miscommunication during the study, alignment information between cEEGrid and PSG recordings has not been saved. This means that to obtain a useful comparison between the two methods, for instance to align the manual scoring with the cEEGrid recordings, some post processing has to be performed. In the derivative dataset, ‘aligned1’, we have shared our own best attempt at alignment. The data set was previously described in the paper ‘Machine-learning-derived sleep–wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy’, Mikkelsen et al 2018, https://doi.org/10.1111/jsr.12786 Contact For questions regarding this data set, contact:
Kaare Mikkelsen, Mikkelsen.kaare@ece.au.dk, https://orcid.org/0000-0002-7360-8629
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 656 h
Signal · Electrodes & live trace#
Live trace viewer — sub-026 · ses-001 · task-sleep
Showing one representative recording out of
20 subjects and 39 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
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 |
Surrey cEEGrid sleep data set |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Kaare B. Mikkelsen, James K Ebajemito, Maria A Bonmati-Carrion, Nayantara Santhi, Victoria L Revell, Giuseppe Atzori, Laura Birch, Ciro Della Monica, Stefan Debener, Derk-Jan Dijk, Annette Sterr, Maarten De Vos |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005207,
title = {Surrey cEEGrid sleep data set},
author = {Kaare B. Mikkelsen and James K Ebajemito and Maria A Bonmati-Carrion and Nayantara Santhi and Victoria L Revell and Giuseppe Atzori and Laura Birch and Ciro Della Monica and Stefan Debener and Derk-Jan Dijk and Annette Sterr and Maarten De Vos},
doi = {10.18112/openneuro.ds005207.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005207.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005207 · Mikkelsen2024_Surrey_cEEGrid_sleepeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005207(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Surrey cEEGrid sleep data set
- Study:
ds005207(OpenNeuro)- Author (year):
Mikkelsen2024_Surrey_cEEGrid_sleep- Canonical:
—
Also importable as:
DS005207,Mikkelsen2024_Surrey_cEEGrid_sleep.Modality:
eeg. Subjects: 20; recordings: 39; 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/ds005207 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005207 DOI: https://doi.org/10.18112/openneuro.ds005207.v1.0.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS005207 >>> dataset = DS005207(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.pytorchdatasets.load_dataset("EEGDash/ds005207").huggingface
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 ds005207 to reproduce the tutorial on this dataset.
Citation
Kaare B. Mikkelsen, James K Ebajemito, Maria A Bonmati-Carrion, Nayantara Santhi, Victoria L Revell, … (20). Surrey cEEGrid sleep data set. 10.18112/openneuro.ds005207.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005207.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset