DS004348: eeg dataset, 9 subjects#

Name: Ear-EEG Sleep Monitoring 2017 (EESM17)
Published: 2022
License: CC0

Ear-EEG Sleep Monitoring 2017 (EESM17)

Access recordings and metadata through EEGDash.

Citation: Kaare B. Mikkelsen, David B. Villadsen, Laura Birch, Marit Otto, Preben Kidmose (2022). Ear-EEG Sleep Monitoring 2017 (EESM17). 10.18112/openneuro.ds004348.v1.0.5

Modality: eeg Subjects: 9 Recordings: 18 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004348

dataset = DS004348(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 = DS004348(cache_dir="./data", subject="01")

Advanced query

dataset = DS004348(
    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{ds004348,
  title = {Ear-EEG Sleep Monitoring 2017 (EESM17)},
  author = {Kaare B. Mikkelsen and David B. Villadsen and Laura Birch and Marit Otto and Preben Kidmose},
  doi = {10.18112/openneuro.ds004348.v1.0.5},
  url = {https://doi.org/10.18112/openneuro.ds004348.v1.0.5},
}

About This Dataset#

Ear-EEG Sleep Monitoring 2017 (EESM17) data set Overview This dataset was collected as part of a research project on ear-EEG sleep monitoring which took place in 2017. The data set contains nightly EEG recordings from 9 healthy participants (‘subjects’). The recordings consist of ‘partial polysomnography’ (PSG) measurements, including EEG, EOG and chin EMG combined with 14 ear-EEG electrodes. 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 subjects were instructed to perform two recordings. In the first recording, they had to simply relax in a chair either reading or watching television, prior to going to bed. These recordings are labeled as ‘wake’ task. After this, the real recording started, which took place during the night and began when the subject went to bed. These recordings are labeled as having task ‘sleep’. The recording equipment was mounted in the afternoon, and the recordings took place at the subject’s home. The data set was previously described in the paper: https://doi.org/10.1186/s12938-017-0400-5 When citing this data set, please refer to this paper. Please note that for all subjects, the sleep scoring begins at ‘Lights out’. Notes Due to a miscommunication in the original sleep study, two ear-EEG channels, ERB1 and ELB1, were not used. However, they are included in the data set. Both electrode positions were very close to the ERB and ELB positions. 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	`DS004348`
Title	Ear-EEG Sleep Monitoring 2017 (EESM17)
Author (year)	`Mikkelsen2022`
Canonical	`EESM17`
Importable as	`DS004348`, `Mikkelsen2022`, `EESM17`
Year	2022
Authors	Kaare B. Mikkelsen, David B. Villadsen, Laura Birch, Marit Otto, Preben Kidmose
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004348.v1.0.5
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: 9
Recordings: 18
Tasks: 2

Channels & sampling rate

Channels: 34
Sampling rate (Hz): 200.0
Duration (hours): 17.527765277777778

API Reference#

Use the DS004348 class to access this dataset programmatically.

class eegdash.dataset.DS004348(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Ear-EEG Sleep Monitoring 2017 (EESM17)

Study:: ds004348 (OpenNeuro)
Author (year):: Mikkelsen2022
Canonical:: EESM17

Also importable as: DS004348, Mikkelsen2022, EESM17.

Modality: eeg. Subjects: 9; recordings: 18; tasks: 2.

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/ds004348 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004348 DOI: https://doi.org/10.18112/openneuro.ds004348.v1.0.5 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004348
>>> dataset = DS004348(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None