EEGdash›NeMAR›ON007176

Iss. 7176 · 45 subjects · 300 recordings · CC0

Dataset Brief · Longitudinal EEG Test-Retest Reliability in Healthy Individuals

ON007176: eeg dataset, 45 subjects#

Longitudinal EEG Test-Retest Reliability in Healthy Individuals

Citation: Verónica Henao Isaza, Valeria Cadavid Castro, Luisa María Zapata Saldarriaga, Yorguin-Jose Mantilla-Ramos, Jazmín Ximena Suarez Revelo, Carlos Andrés Tobón Quintero, John Fredy Ochoa Gómez (20). Longitudinal EEG Test-Retest Reliability in Healthy Individuals. 10.82901/nemar.on007176

45-participant EEG dataset — Longitudinal EEG Test-Retest Reliability in Healthy Individuals.

Data & curation Verónica Henao Isaza · Valeria Cadavid Castro · Luisa María Zapata Saldarriaga · Yorguin-Jose Mantilla-Ramos · Jazmín Ximena Suarez Revelo · Carlos Andrés Tobón Quintero · …
Year 20 · Distributed via NeMAR
Funding Comité para el Desarrollo de la Investigación - CODI, Universidad de Antioquia

EEG · 60 ch1000 HzBIDS 1.8.02 tasks5 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 ON007176

dataset = ON007176(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 = ON007176(cache_dir="./data", subject="01")

Advanced query

dataset = ON007176(
    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{on007176,
  title = {Longitudinal EEG Test-Retest Reliability in Healthy Individuals},
  author = {Verónica Henao Isaza and Valeria Cadavid Castro and Luisa María Zapata Saldarriaga and Yorguin-Jose Mantilla-Ramos and Jazmín Ximena Suarez Revelo and Carlos Andrés Tobón Quintero and John Fredy Ochoa Gómez},
  doi = {10.82901/nemar.on007176},
  url = {https://doi.org/10.82901/nemar.on007176},
}

§ 02Study · The README

About This Dataset#

This dataset contains longitudinal resting-state EEG recordings from 43 healthy adults,

collected over four sessions spanning approximately two years, with an average interval of 7.2 months between sessions. The dataset includes raw EEG data and relevant metadata following the BIDS standard.

The dataset was acquired to assess the test-retest reliability of EEG signals using

an automated preprocessing pipeline, including independent component analysis and wavelet-enhanced artifact removal. It allows for analysis of neural components, relative power in regions of interest (ROIs), and longitudinal stability of EEG measures.

DOI

Longitudinal EEG Test-Retest Reliability in Healthy Individuals

Dataset Description

Data Structure

dataset_description.json : Dataset metadata and authorship information.

View full README

DOI

Longitudinal EEG Test-Retest Reliability in Healthy Individuals

Dataset Description

Data Structure

dataset_description.json : Dataset metadata and authorship information.

participants.tsv : Participant demographics and IDs.

sub-XX/eeg/ : Folder for each participant containing EEG data files.

EEG Data

Each participant folder contains EEG recordings in BIDS-compliant format. Data include: - Raw EEG signals (.eeg, .vhdr, .vmrk) - Associated metadata files (.json) describing recording parameters and task information.

Usage Notes

All participants provided written informed consent.

Data are de-identified and do not contain personally identifiable information.

Users should cite the following paper when using this dataset: Henao Isaza V, et al. Longitudinal test-retest reliability of quantitative EEG in healthy individuals using an automated preprocessing approach. DOI: 10.1016/j.bspc.2026.109484

License

This dataset is publicly available under a Creative Commons CC0 license.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=45, range 21–62 yr, mean 36.4 yr · sex per subject not reported)

202530354045505560

Sex composition

Female
28
Male
17
F : M ratio
1.65 : 1

62% female · n = 45 subjects with reported sex.

Channel counts: 60 ch (n=300 recordings)

Sampling frequencies: 1000.0 Hz (n=300 recordings)

Total recording duration: 26 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 60 ch · EEG · 1000 Hz · 45 subjects, 300 recordings

Live trace viewer — sub-CTR001 · ses-V0 · task-CE

Showing one representative recording out of 45 subjects and 300 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 · 58 sensors — 58 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.ON007176(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON007176

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

class eegdash.dataset.ON007176(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Longitudinal EEG Test-Retest Reliability in Healthy Individuals

Study:: on007176 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: ON007176, nan.

Modality: eeg. Subjects: 45; recordings: 300; 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/on007176 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007176 DOI: https://doi.org/10.82901/nemar.on007176

Examples

>>> from eegdash.dataset import ON007176
>>> dataset = ON007176(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 — ON007176.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 on007176 to reproduce the tutorial on this dataset.

Citation

Verónica Henao Isaza, Valeria Cadavid Castro, Luisa María Zapata Saldarriaga, Yorguin-Jose Mantilla-Ramos, Jazmín Ximena Suarez Revelo, … (20). Longitudinal EEG Test-Retest Reliability in Healthy Individuals. 10.82901/nemar.on007176

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on007176.

Related & sibling datasets

NM000132EEG · 40 subj ON003825EEG · 50 subj ON007181EEG · 59 subj NM000180EEG · 45 subj ON004018EEG · 16 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

channels · eeg.json

Provenance

CC0 · 10.82901/nemar.on007176

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON007176`
Title	Longitudinal EEG Test-Retest Reliability in Healthy Individuals
Author (year)	—
Canonical	—
Importable as	`ON007176`
Year	20
Authors	Verónica Henao Isaza, Valeria Cadavid Castro, Luisa María Zapata Saldarriaga, Yorguin-Jose Mantilla-Ramos, Jazmín Ximena Suarez Revelo, Carlos Andrés Tobón Quintero, John Fredy Ochoa Gómez
License	CC0
Citation / DOI	10.82901/nemar.on007176
Source links	OpenNeuro \| NeMAR \| Source URL

ON007176: eeg dataset, 45 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#