EEGdash›OpenNeuro›DS004348

Iss. 4348 · 9 subjects · 18 recordings · CC0

Dataset Brief · Ear-EEG Sleep Monitoring 2017 (EESM17)

DS004348: eeg dataset, 9 subjects#

Ear-EEG Sleep Monitoring 2017 (EESM17)

Citation: Kaare B. Mikkelsen, David B. Villadsen, Laura Birch, Marit Otto, Preben Kidmose (20). Ear-EEG Sleep Monitoring 2017 (EESM17). 10.18112/openneuro.ds004348.v1.0.5

9-participant EEG dataset — Ear-EEG Sleep Monitoring 2017 (EESM17).

Data & curation Kaare B. Mikkelsen · David B. Villadsen · Laura Birch · Marit Otto · Preben Kidmose
Year 20 · Distributed via OpenNeuro

EEG · 34 ch200 HzBIDS 1.7.02 tasksHealthySleepSleep

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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},
}

§ 02Study · The README

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Sex composition

Female
3
Male
6
F : M ratio
0.50 : 1

33% female · n = 9 subjects with reported sex.

Channel counts: 34 ch (n=9 recordings)

Sampling frequencies: 200.0 Hz (n=9 recordings)

Total recording duration: 52 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 34 ch · EEG · 200 Hz · 9 subjects, 18 recordings

Live trace viewer — sub-002 · ses-001 · task-wake

Showing one representative recording out of 9 subjects and 18 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS004348(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Mikkelsen2022

Canonical—

Importable asDS004348 · Mikkelsen2022

Sourceeegdash/dataset/registry.py · [source ↗]

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

Ear-EEG Sleep Monitoring 2017 (EESM17)

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

Also importable as: DS004348, Mikkelsen2022.

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: 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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FacePre-bundled mirror at EEGDash/ds004348 · pull with datasets.load_dataset("EEGDash/ds004348").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004348.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 ds004348 to reproduce the tutorial on this dataset.

Citation

Kaare B. Mikkelsen, David B. Villadsen, Laura Birch, Marit Otto, Preben Kidmose (20). Ear-EEG Sleep Monitoring 2017 (EESM17). 10.18112/openneuro.ds004348.v1.0.5

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds004348.v1.0.5.

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DS003626EEG · 10 subj DS003195EEG · 10 subj DS003194EEG · 15 subj DS003810EEG · 10 subj DS002218EEG · 18 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds004348.v1.0.5

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004348`
Title	Ear-EEG Sleep Monitoring 2017 (EESM17)
Author (year)	`Mikkelsen2022`
Canonical	—
Importable as	`DS004348`, `Mikkelsen2022`
Year	20
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

DS004348: eeg dataset, 9 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#