DS005178: eeg dataset, 10 subjects#
Ear-EEG Sleep Monitoring 2023 (EESM23)
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 (20). Ear-EEG Sleep Monitoring 2023 (EESM23). 10.18112/openneuro.ds005178.v1.0.0
10-participant EEG dataset — Ear-EEG Sleep Monitoring 2023 (EESM23).
Quickstart#
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)
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'])
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. Task description The patient performed tasks prior to going to bed. These recordings are labeled with ‘video’ as task. After his, 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’.
For the first two recordings, the recording equipment was mounted in the afternoon. For the remaining recordings, the subject mounted the ear-EEG equipment by themselves immediately prior to going to bed. All recordings took place at the subject’s home. As can be seen in the diaries accompanying the recordings, the subjects wrote down recording start, electrode test start, when they went to bed, lights-out and recording end, and marked these in the data files using the trigger button on the equipment. 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 Contact For questions regarding this data set, contact:
Preben Kidmose, pki@ece.au.dk, https://orcid.org/0000-0001-8628-8057 Kaare Mikkelsen, Mikkelsen.kaare@ece.au.dk, https://orcid.org/0000-0002-7360-8629
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies: 250.0 Hz (n=140 recordings)
Total recording duration: 1012 h
Signal · Electrodes & live trace#
Live trace viewer — sub-010 · ses-012 · task-sleep
Showing one representative recording out of
10 subjects and 140 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.
Electrode layout — EEG · 8 sensors — 8 channels
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 |
Ear-EEG Sleep Monitoring 2023 (EESM23) |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
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 |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005178 · Tabar2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005178(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Ear-EEG Sleep Monitoring 2023 (EESM23)
- Study:
ds005178(OpenNeuro)- Author (year):
Tabar2024- Canonical:
—
Also importable as:
DS005178,Tabar2024.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
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/ds005178 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005178 DOI: https://doi.org/10.18112/openneuro.ds005178.v1.0.0
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: 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/ds005178").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 ds005178 to reproduce the tutorial on this dataset.
Citation
Yousef Rezaei Tabar, Kaare Mikkelsen, Laura Birch, Nelly Shenton, Simon L Kappel, … (20). Ear-EEG Sleep Monitoring 2023 (EESM23). 10.18112/openneuro.ds005178.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.ds005178.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset