EEGdash›OpenNeuro›DS007987

Iss. 7987 · 43 subjects · 172 recordings · CC0

Dataset Brief · Raw resting-state EEG dataset with alternating eyes-open and…

DS007987: eeg dataset, 43 subjects#

Raw resting-state EEG dataset with alternating eyes-open and eyes-closed recordings in healthy adults

Access recordings and metadata through EEGDash.

Citation: Lydia Arana, Enrique Stern, Almudena Capilla (2026). Raw resting-state EEG dataset with alternating eyes-open and eyes-closed recordings in healthy adults. 10.18112/openneuro.ds007987.v1.1.0

Modality: eeg Subjects: 43 Recordings: 172 License: CC0 Source: openneuro

Metadata: Complete (100%)

43-participant EEG dataset — Raw resting-state EEG dataset with alternating eyes-open and eyes-closed recordings in healthy adults.

Data & curation Lydia Arana · Enrique Stern · Almudena Capilla
Senior author Almudena Capilla
Year 2026 · Distributed via OpenNeuro
Funding This work was supported by Ministerio de Ciencia, Innovación y Universidades / Agencia Estatal de Investigación, Spain / FEDER/FSE+, UE (MCIU/AEI/10.13039/501100011033 /FEDER/FSE+, UE; PID2024-161032NB-I00) and the Comunidad de Madrid, Spain (IND2022/SOC-23652)

EEG · 144 ch1024, 2048 HzBIDS 1.8.0Task · resting2 sessions

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 DS007987

dataset = DS007987(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 = DS007987(cache_dir="./data", subject="01")

Advanced query

dataset = DS007987(
    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{ds007987,
  title = {Raw resting-state EEG dataset with alternating eyes-open and eyes-closed recordings in healthy adults},
  author = {Lydia Arana and Enrique Stern and Almudena Capilla},
  doi = {10.18112/openneuro.ds007987.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds007987.v1.1.0},
}

§ 02Study · The README

About This Dataset#

This dataset contains raw EEG recordings in resting state from healthy participants with no current psychiatric or neurological diagnosis, recorded at the Universidad Autónoma de Madrid, inside a Faraday cage.

No preprocessing was applied prior to data sharing, only conversion to bids from .bdf format (.bdf files are also available upon request).

Dataset organization:

All EEG recordings were acquired during a single recording session lasting approximately 20 minutes. Participants completed four consecutive 5-minute runs: two eyes-open (EO) runs and two eyes-closed (EC) runs. Conditions alternated throughout the session, and participants were randomly assigned to one of two acquisition sequences: EO run-1 → EC run-1 → EO run-2 → EC run-2, or EC run-1 → EO run-1 → EC run-2 → EO run-2.

In the BIDS structure, recordings are organized into two session folders for each participant: “ses-OA” (Ojos Abiertos: eyes open) and “ses-OC” (Ojos Cerrados: eyes closed). These session labels are used only to separate recording conditions and do not indicate different visits or recording days. Example: sub-05/

├── ses-OA/
│ └── eeg/
│   ├── ...run-1...
│   └── ...run-2...
└── ses-OC/
  └── eeg/
    ├── ...run-1...
    └── ...run-2...

Note that file names also use Spanish abbreviations: OA = Ojos Abiertos (eyes open) and OC = Ojos Cerrados (eyes closed). - Recording system:

Biosemi ActiveTwo 128 channels. 1024 Hz sampling rate (except for subj 19, 20, 31, 33, 37, 38, 39, 40, 41, 42, 47 with 2048 Hz).

Note that the power line frequency in Spain is 50 Hz (and harmonics). Dim light (120-150 lux).

Participants were instructed to sit still and avoid muscle tension. Black fixation point over white wall for OA condition.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=43, range 18–30 yr, mean 20.1 yr)

15202530

Female · 25Male · 18

Sex composition

Female
25
Male
18
F : M ratio
1.39 : 1

58% female · n = 43 subjects with reported sex.

Channel counts: 144 ch (n=172 recordings)

Sampling frequencies (Hz)

10242048

Total recording duration: 15 h 25 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 144 ch · EEG · 1024, 2048 Hz · 43 subjects, 172 recordings

Live trace viewer — sub-17 · ses-OA · task-resting · run-1

Showing one representative recording out of 43 subjects and 172 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 · 128 sensors — 128 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

§ 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.DS007987(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS007987

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

class eegdash.dataset.DS007987(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Raw resting-state EEG dataset with alternating eyes-open and eyes-closed recordings in healthy adults

Study:: ds007987 (OpenNeuro)
Author (year):: —
Canonical:: —

Also importable as: DS007987.

Modality: eeg; Subject type: Unknown. Subjects: 43; recordings: 172; 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/ds007987 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007987 DOI: https://doi.org/10.18112/openneuro.ds007987.v1.1.0

Examples

>>> from eegdash.dataset import DS007987
>>> dataset = DS007987(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 — DS007987.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 ds007987 to reproduce the tutorial on this dataset.

Citation

Lydia Arana, Enrique Stern, Almudena Capilla (2026). Raw resting-state EEG dataset with alternating eyes-open and eyes-closed recordings in healthy adults. 10.18112/openneuro.ds007987.v1.1.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds007987.v1.1.0.

Related & sibling datasets

DS007864EEG · 45 subj DS008465EEG · 30 subj DS007822EEG · 33 subj DS007865EEG · 24 subj DS008014EEG · 78 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

events · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007987.v1.1.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007987`
Title	Raw resting-state EEG dataset with alternating eyes-open and eyes-closed recordings in healthy adults
Author (year)	—
Canonical	—
Importable as	`DS007987`
Year	2026
Authors	Lydia Arana, Enrique Stern, Almudena Capilla
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007987.v1.1.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS007987: eeg dataset, 43 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#