DS007020#

EEG Mortality Dataset in Parkinson’s Disease

Access recordings and metadata through EEGDash.

Citation: Simin Jamshidi, Arturo Espinoza, Soura Dasgupta, Nandakumar Narayanan (2025). EEG Mortality Dataset in Parkinson’s Disease. 10.18112/openneuro.ds007020.v1.0.0

Modality: eeg Subjects: 94 Recordings: 945 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007020

dataset = DS007020(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 = DS007020(cache_dir="./data", subject="01")

Advanced query

dataset = DS007020(
    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{ds007020,
  title = {EEG Mortality Dataset in Parkinson's Disease},
  author = {Simin Jamshidi and Arturo Espinoza and Soura Dasgupta and Nandakumar Narayanan},
  doi = {10.18112/openneuro.ds007020.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007020.v1.0.0},
}

About This Dataset#

This dataset contains de-identified resting-state EEG recordings from individuals with Parkinson’s disease (PD) and age-matched healthy control subjects. All EEG data were recorded using standard clinical EEG systems at Neurology Clinic. Dataset Purpose: This dataset was originally used to evaluate whether resting-state EEG can help distinguish subjects who were later deceased from those who remained living (mortality classification). Only de-identified EEG data and mortality labels are included.

Participant Information: - Participants are labeled as either “living” or “deceased” in participants.tsv - No other demographic or clinical information (age, cognition, UPDRS, disease duration, etc.) is included per data-sharing guidelines. - All participant IDs are anonymized following BIDS convention (e.g., sub-PD1301).

EEG Acquisition Details:

Dataset Information#

Dataset ID	`DS007020`
Title	EEG Mortality Dataset in Parkinson’s Disease
Year	2025
Authors	Simin Jamshidi, Arturo Espinoza, Soura Dasgupta, Nandakumar Narayanan
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007020.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: 94
Recordings: 945
Tasks: 1

Channels & sampling rate

Channels: 63 (170), 64 (18)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS007020 class to access this dataset programmatically.

class eegdash.dataset.DS007020(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds007020. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. Subjects: 94; recordings: 94; 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/ds007020 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007020

Examples

>>> from eegdash.dataset import DS007020
>>> dataset = DS007020(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