DS004584#

Rest eyes open

Access recordings and metadata through EEGDash.

Citation: Arun Singh arun.singh@usd.edu, Rachel Cole rachel-cole@uiowa.edu, Arturo Espinoza arturo-espinoza@uiowa.edu, Jim Cavanagh jcavanagh@unm.edu, Nandakumar Narayanan nandakumar-narayanan@uiowa.edu (2023). Rest eyes open. 10.18112/openneuro.ds004584.v1.0.0

Modality: eeg Subjects: 149 Recordings: 1049 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004584

dataset = DS004584(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 = DS004584(cache_dir="./data", subject="01")

Advanced query

dataset = DS004584(
    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{ds004584,
  title = {Rest eyes open},
  author = {Arun Singh arun.singh@usd.edu and Rachel Cole rachel-cole@uiowa.edu and Arturo Espinoza arturo-espinoza@uiowa.edu and Jim Cavanagh jcavanagh@unm.edu and Nandakumar Narayanan nandakumar-narayanan@uiowa.edu},
  doi = {10.18112/openneuro.ds004584.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004584.v1.0.0},
}

About This Dataset#

This experiment includes 149 subjects: 100 individuals with Parkinsons disease, and 49 controls. EEG was recorded with a 64-channel BrainVision cap. Resting-state EEG was collected from patients sitting in a quiet room with their eyes open for two minutes.

Dataset Information#

Dataset ID	`DS004584`
Title	Rest eyes open
Year	2023
Authors	Arun Singh arun.singh@usd.edu, Rachel Cole rachel-cole@uiowa.edu, Arturo Espinoza arturo-espinoza@uiowa.edu, Jim Cavanagh jcavanagh@unm.edu, Nandakumar Narayanan nandakumar-narayanan@uiowa.edu
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004584.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

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: 149
Recordings: 1049
Tasks: 1

Channels & sampling rate

Channels: 63 (238), 64 (58), 66 (2)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS004584 class to access this dataset programmatically.

class eegdash.dataset.DS004584(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004584. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. Subjects: 149; recordings: 149; 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/ds004584 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004584

Examples

>>> from eegdash.dataset import DS004584
>>> dataset = DS004584(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