DS005178#

Ear-EEG Sleep Monitoring 2023 (EESM23)

Access recordings and metadata through EEGDash.

Citation: Yousef Rezaei Tabar, Kaare Mikkelsen, Laura Birch, Nelly Shenton, Simon L Kappel, Astrid R Bertelsen, Reza Nikbakht, Hans O Toft, Chris H Henriksen, Martin C Hemmsen, Mike L Rank, Marit Otto, Preben Kidmose (2024). Ear-EEG Sleep Monitoring 2023 (EESM23). 10.18112/openneuro.ds005178.v1.0.0

Modality: eeg Subjects: 10 Recordings: 707 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005178

dataset = DS005178(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 = DS005178(cache_dir="./data", subject="01")

Advanced query

dataset = DS005178(
    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{ds005178,
  title = {Ear-EEG Sleep Monitoring 2023 (EESM23)},
  author = {Yousef Rezaei Tabar and Kaare Mikkelsen and Laura Birch and Nelly Shenton and Simon L Kappel and Astrid R Bertelsen and Reza Nikbakht and Hans O Toft and Chris H Henriksen and Martin C Hemmsen and Mike L Rank and Marit Otto and Preben Kidmose},
  doi = {10.18112/openneuro.ds005178.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005178.v1.0.0},
}

About This Dataset#

Ear-EEG Sleep Monitoring 2023 (EESM23) data set

Overview

This dataset was collected as part of a research project on ear-EEG sleep monitoring which took place in 2020-2022.

The data set contains nightly EEG recordings from 10 healthy participants (‘subjects’). The first two recordings consist of polysomnogrpahy (PSG) measurements and ear-EEG measurements. The remaining ten recordings consist of only ear-EEG measurements, though a few subjects were asked to repeat a recording. Only the accepted recordings can be found in the BIDS formatted data set.

Each file consists of a video sequence followed by a sleep sequence. After the video sequence, the subject sent triggers to distinguish between the two sequences. Due to potential variability in triggering the device, the sequences remain in one file though it should be possible to manually sort the file into distinct video and sleep sequences. There are no events.tsv files for Ear-EEG.

Dataset Information#

Dataset ID	`DS005178`
Title	Ear-EEG Sleep Monitoring 2023 (EESM23)
Year	2024
Authors	Yousef Rezaei Tabar, Kaare Mikkelsen, Laura Birch, Nelly Shenton, Simon L Kappel, Astrid R Bertelsen, Reza Nikbakht, Hans O Toft, Chris H Henriksen, Martin C Hemmsen, Mike L Rank, Marit Otto, Preben Kidmose
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005178.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: 10
Recordings: 707
Tasks: 1

Channels & sampling rate

Channels: 4 (240), 13 (40)
Sampling rate (Hz): 250.0
Duration (hours): 0.0

API Reference#

Use the DS005178 class to access this dataset programmatically.

class eegdash.dataset.DS005178(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005178. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 10; recordings: 140; 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/ds005178 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005178

Examples

>>> from eegdash.dataset import DS005178
>>> dataset = DS005178(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