EEGdash›OpenNeuro›DS008108

Iss. 8108 · 142 subjects · 142 recordings · CC0

Dataset Brief · A Comprehensive Multimodal Dataset for Investigating Cognitiv…

DS008108: eeg dataset, 142 subjects#

A Comprehensive Multimodal Dataset for Investigating Cognitive Impairment in Obstructive Sleep Apnea

Access recordings and metadata through EEGDash.

Citation: Wei Guo, Wei Tian, Wanqi Chen, Tao Jiang, Dong Chen, Fengyu Cong, Fan Li (2026). A Comprehensive Multimodal Dataset for Investigating Cognitive Impairment in Obstructive Sleep Apnea. 10.18112/openneuro.ds008108.v1.0.0

Modality: eeg Subjects: 142 Recordings: 142 License: CC0 Source: openneuro

Metadata: Complete (90%)

142-participant EEG dataset — A Comprehensive Multimodal Dataset for Investigating Cognitive Impairment in Obstructive Sleep Apnea.

Data & curation Wei Guo · Wei Tian · Wanqi Chen · Tao Jiang · Dong Chen · Fengyu Cong · …
Senior author Fan Li
Year 2026 · Distributed via OpenNeuro

128, 256 HzBIDS 1.9.0Task · sleep

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 DS008108

dataset = DS008108(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 = DS008108(cache_dir="./data", subject="01")

Advanced query

dataset = DS008108(
    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{ds008108,
  title = {A Comprehensive Multimodal Dataset for Investigating Cognitive Impairment in Obstructive Sleep Apnea},
  author = {Wei Guo and Wei Tian and Wanqi Chen and Tao Jiang and Dong Chen and Fengyu Cong and Fan Li},
  doi = {10.18112/openneuro.ds008108.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds008108.v1.0.0},
}

§ 02Study · The README

About This Dataset#

The dataset includes behavioral, polysomnography (PSG), neuroimaging, and questionnaire recordings collected from 142 participants, including patients with OSA and matched healthy controls.All data have been rigorously curated to comply with the Brain Imaging Data Structure (BIDS) standard.

A Comprehensive Multimodal Dataset for Investigating Cognitive Impairment in Obstructive Sleep Apnea

2. Experimental Design (Longitudinal Sessions)

The experimental protocol utilizes a test-retest multi-session design spanning a 24-hour period to support the investigation of cognitive impairment in OSA.The directory structure is organized into the following sessions (ses-): * ses-preSleep: Evening baseline cognitive assessment. * ses-nightSleep: Nocturnal polysomnography (PSG) recording. * ses-postSleep: Morning cognitive assessment immediately following nocturnal sleep. * ses-preNap: Early afternoon cognitive assessment prior to a diurnal nap. * ses-postNap: Late afternoon assessment following the nap, including structural and resting-state functional MRI scans.

View full README

A Comprehensive Multimodal Dataset for Investigating Cognitive Impairment in Obstructive Sleep Apnea

2. Experimental Design (Longitudinal Sessions)

The experimental protocol utilizes a test-retest multi-session design spanning a 24-hour period to support the investigation of cognitive impairment in OSA.The directory structure is organized into the following sessions (ses-): * ses-preSleep: Evening baseline cognitive assessment. * ses-nightSleep: Nocturnal polysomnography (PSG) recording. * ses-postSleep: Morning cognitive assessment immediately following nocturnal sleep. * ses-preNap: Early afternoon cognitive assessment prior to a diurnal nap. * ses-postNap: Late afternoon assessment following the nap, including structural and resting-state functional MRI scans.

3. Cognitive Assessment Battery (task-cognitive)

During the waking sessions (ses-preSleep, ses-postSleep, ses-preNap, ses-postNap), participants completed a continuous cognitive assessment battery: 1. PPT: Picture Pairing Task 2. SART: Sustained Attention to Response Task 3. MST: Motor Skills Task 4. PVT: Psychomotor Vigilance Task

4. Data Modalities & Technical Notes

* **Polysomnography (PSG)**: Standard multi-channel PSG (.edf) was recorded continuously during the ses-nightSleep session. * **Magnetic Resonance Imaging (MRI)**: High-resolution T1-weighted structural scans (anat) and resting-state BOLD functional scans (func) were acquired once at the ses-postNap timepoint to investigate structural divergence and cognitive network reorganization. * **Behavioral Data (beh)**: Task performance metrics (e.g., reaction times, error rates) are provided in tab-separated .tsv format, isolated by session.

5. Directory Structure Highlight

dataset/

├── README.md
├── dataset_description.json
├── sub-001/
│   ├── ses-preSleep/  (Behavioral)
│   ├── ses-nightSleep/ (PSG EDF)
│   ├── ses-postSleep/ (Behavioral)
│   ├── ses-preNap/    (Behavioral)
│   └── ses-postNap/   (Behavioral, T1w MRI, rs-fMRI)
└── ...

6. Contributors

* Wei Guo, , Wei Tian, Wanqi Chen, Tao Jiang, Dong Chen, Fengyu Cong, and Fan Li

Institution: School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology

7. License and Usage

This dataset is released under the CC0 license. If you use this data in your research or utilize our custom analysis pipelines, please cite the accompanying dataset descriptor publication and the provided Dataset DOI.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=141, range 19–65 yr, mean 29.9 yr)

1520253035404550556065

Other · 141

Sex composition

Female
53
Male
89
F : M ratio
0.60 : 1

37% female · n = 142 subjects with reported sex.

HandednessRight · 132Left · 3

Sampling frequencies (Hz)

128256

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage — ch · EEG · 128, 256 Hz · 142 subjects, 142 recordings

Live trace viewer — sub-098 · ses-nightSleep · task-sleep

Showing one representative recording out of 142 subjects and 142 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.DS008108(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS008108

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

class eegdash.dataset.DS008108(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

A Comprehensive Multimodal Dataset for Investigating Cognitive Impairment in Obstructive Sleep Apnea

Study:: ds008108 (OpenNeuro)
Author (year):: —
Canonical:: —

Also importable as: DS008108.

Modality: eeg; Subject type: Unknown. Subjects: 142; recordings: 142; 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/ds008108 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds008108 DOI: https://doi.org/10.18112/openneuro.ds008108.v1.0.0

Examples

>>> from eegdash.dataset import DS008108
>>> dataset = DS008108(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 FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS008108.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 ds008108 to reproduce the tutorial on this dataset.

Citation

Wei Guo, Wei Tian, Wanqi Chen, Tao Jiang, Dong Chen, … (2026). A Comprehensive Multimodal Dataset for Investigating Cognitive Impairment in Obstructive Sleep Apnea. 10.18112/openneuro.ds008108.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.ds008108.v1.0.0.

Related & sibling datasets

DS007823EEG · 173 subj DS008115EEG · 180 subj DS007827EEG · 99 subj DS008730EEG · 104 subj DS008092EEG · 77 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds008108.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS008108`
Title	A Comprehensive Multimodal Dataset for Investigating Cognitive Impairment in Obstructive Sleep Apnea
Author (year)	—
Canonical	—
Importable as	`DS008108`
Year	2026
Authors	Wei Guo, Wei Tian, Wanqi Chen, Tao Jiang, Dong Chen, Fengyu Cong, Fan Li
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds008108.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS008108: eeg dataset, 142 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#