DS006525#

Resting EEG

Access recordings and metadata through EEGDash.

Citation: Computational Neuroimaging and Neuroengineering Lab ar the University of Oklahoma (2025). Resting EEG. 10.18112/openneuro.ds006525.v1.0.0

Modality: eeg Subjects: 34 Recordings: 311 License: CC0 Source: openneuro

Metadata: Complete (100%)

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.

Dataset Information#

Dataset ID

DS006525

Title

Resting EEG

Year

2025

Authors

Computational Neuroimaging and Neuroengineering Lab ar the University of Oklahoma

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006525.v1.0.0

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},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 34

  • Recordings: 311

  • Tasks: 2

Channels & sampling rate

  • Channels: 128 (60), 129 (8)

  • Sampling rate (Hz): 250.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: Resting State

  • Type: Resting-state

Files & format

  • Size on disk: 3.0 GB

  • File count: 311

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006525.v1.0.0

Provenance

API Reference#

Use the DS006525 class to access this dataset programmatically.

class eegdash.dataset.DS006525(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006525. 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

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/ds006525 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006525

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#