DS005207: eeg dataset, 20 subjects#

Surrey cEEGrid sleep data set

Access recordings and metadata through EEGDash.

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 (2024). Surrey cEEGrid sleep data set. 10.18112/openneuro.ds005207.v1.0.0

Modality: eeg Subjects: 20 Recordings: 39 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

Dataset Information#

Dataset ID	`DS005207`
Title	Surrey cEEGrid sleep data set
Author (year)	`Mikkelsen2024_Surrey_cEEGrid_sleep`
Canonical	—
Importable as	`DS005207`, `Mikkelsen2024_Surrey_cEEGrid_sleep`
Year	2024
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	doi:10.18112/openneuro.ds005207.v1.0.0
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: 20
Recordings: 39
Tasks: 1

Channels & sampling rate

Channels: 13 (8), 24 (6), 20 (5), 11 (5), 27 (4), 21 (3), 18 (3), 23 (2), 15 (2), 22
Sampling rate (Hz): 128.0 (20), 250.0 (19)
Duration (hours): 422.5796577083333

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#

Channel counts (ch)

11131518202122232427

Sampling frequencies (Hz)

128250

Total recording duration: 422 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 DS005207 class to access this dataset programmatically.

class eegdash.dataset.DS005207(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

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/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)