DS006525: eeg dataset, 34 subjects#
Resting EEG
Citation: Computational Neuroimaging and Neuroengineering Lab ar the University of Oklahoma (—). Resting EEG. 10.18112/openneuro.ds006525.v1.0.0
34-participant EEG dataset — Resting EEG.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006525
dataset = DS006525(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006525(cache_dir="./data", subject="01")
Advanced query
dataset = DS006525(
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{ds006525,
title = {Resting EEG},
author = {Computational Neuroimaging and Neuroengineering Lab ar the University of Oklahoma},
doi = {10.18112/openneuro.ds006525.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds006525.v1.0.0},
}
About This Dataset#
Introduction: The EEG data was recorded using the 128-channel Amps 300 amplifier (Electrical Geodesics Inc., OR, USA) at a sampling frequency of 1000 Hz.
The EEG data acquisition was conducted during the resting.
Structural MRI data for the same participants were acquired at the University of Oklahoma Health Science Center (OUHSC) MRI facility using a GE MR750 scanner. The scans were obtained with GE’s BRAVO sequence, with a field of view (FOV) of 240 mm and 180 axial slices per slab Preprocessing in EEGLAB: After the data acquisition, a band-pass filter (0.5–100 Hz) and a notch filter (58–62 Hz) were applied to remove noise.
Noisy channels and artifacts (e.g., from eye blinks, muscle movements, or heartbeats) were identified and removed. Bad channels were replaced using interpolation, and the data was re-referenced to the average of all electrodes.
The data was then sampled down to 250 Hz to reduce file size while keeping enough detail. No data segments were removed to ensure the continuity needed for later analysis.
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies: 250.0 Hz (n=34 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-026 · task-resting
Showing one representative recording out of
34 subjects and 34 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 · 129 sensors — 129 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 |
Resting EEG |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Computational Neuroimaging and Neuroengineering Lab ar the University of Oklahoma |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006525,
title = {Resting EEG},
author = {Computational Neuroimaging and Neuroengineering Lab ar the University of Oklahoma},
doi = {10.18112/openneuro.ds006525.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds006525.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006525 · Neuroimaging2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006525(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Resting EEG
- Study:
ds006525(OpenNeuro)- Author (year):
Neuroimaging2025- Canonical:
—
Also importable as:
DS006525,Neuroimaging2025.Modality:
eeg; Experiment type:Resting-state; Subject type:Unknown. Subjects: 34; recordings: 34; 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/ds006525 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006525 DOI: https://doi.org/10.18112/openneuro.ds006525.v1.0.0
Examples
>>> from eegdash.dataset import DS006525 >>> dataset = DS006525(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/ds006525").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 ds006525 to reproduce the tutorial on this dataset.
Citation
Computational Neuroimaging and Neuroengineering Lab ar the University of Oklahoma (n.d.). Resting EEG. 10.18112/openneuro.ds006525.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.ds006525.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset