EEGdash›NeMAR›ON008768

Iss. 8768 · 312 subjects · 358 recordings · CC0

Dataset Brief · Resting-State EEG in Parkinson's Disease and Healthy Controls

ON008768: eeg dataset, 312 subjects#

Resting-State EEG in Parkinson’s Disease and Healthy Controls

Access recordings and metadata through EEGDash.

Citation: Brooke Yeager, Arturo Espinoza, Rachel Cole, Christina Weber, Ergun Uc, James Cavanagh, Nandakumar Narayanan (—). Resting-State EEG in Parkinson’s Disease and Healthy Controls. 10.82901/nemar.on008768

Modality: eeg Subjects: 312 Recordings: 358 License: CC0 Source: nemar

Metadata: Complete (100%)

312-participant EEG dataset — Resting-State EEG in Parkinson's Disease and Healthy Controls.

Data & curation Brooke Yeager · Arturo Espinoza · Rachel Cole · Christina Weber · Ergun Uc · James Cavanagh · …
Senior author Nandakumar Narayanan

EEG · 63 (187), 64 (170), 66 ch500 Hz · mixedBIDS 1.11.1Task · rest4 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 ON008768

dataset = ON008768(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 = ON008768(cache_dir="./data", subject="01")

Advanced query

dataset = ON008768(
    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{on008768,
  title = {Resting-State EEG in Parkinson's Disease and Healthy Controls},
  author = {Brooke Yeager and Arturo Espinoza and Rachel Cole and Christina Weber and Ergun Uc and James Cavanagh and Nandakumar Narayanan},
  doi = {10.82901/nemar.on008768},
  url = {https://doi.org/10.82901/nemar.on008768},
}

§ 02Study · The README

About This Dataset#

This dataset contains human resting-state electroencephalography (EEG) recordings collected from individuals with Parkinson’s disease (PD) and healthy controls (HC). Recordings were acquired during eyes-open resting-state conditions.

The dataset includes 358 resting-state EEG recordings from 312 unique participants. Some participants have recordings from multiple sessions, allowing for longitudinal analyses.

DOI

Resting-State EEG in Parkinson’s Disease and Healthy Controls

Overview

Participants

The dataset includes participants with Parkinson’s disease and healthy control participants. Participant-level demographic information is provided in participants.tsv.

View full README

DOI

Resting-State EEG in Parkinson’s Disease and Healthy Controls

Overview

Participants

The dataset includes participants with Parkinson’s disease and healthy control participants. Participant-level demographic information is provided in participants.tsv.

Participant-level variables include: - Age at the first resting-state EEG session - Sex - Education - Race - Group (PD or Control)

Session-specific clinical and acquisition information is provided in each participant’s _sessions.tsv file.

Session-level variables include: - Age at the time of recording - Montreal Cognitive Assessment (MoCA) score - UPDRS Part III score - Parkinson’s disease duration - Levodopa equivalent daily dose (LEDD) - Resting-state condition - De-identified acquisition date - Days since the participant’s first session

EEG Data

EEG recordings are provided in BrainVision format (.vhdr, .vmrk, and .eeg) and organized according to the Brain Imaging Data Structure (BIDS) specification.

The majority of recordings were acquired at 500 Hz. A subset of recordings was acquired at 25,000 Hz. Original sampling frequencies have been retained. EEG recordings use Pz as the reference electrode. Recordings contain between 63 and 64 EEG channels, with some recordings additionally containing auxiliary channels such as audio or respiratory measurements. These channels have been retained where available.

Recordings were collected using eyes-open resting-state conditions.

Dataset Organization

The dataset follows BIDS version 1.11.1.

At the dataset level: - participants.tsv contains one row per participant. - participants.json describes the participant-level variables. - Participant-specific _sessions.tsv files contain session-level demographic, clinical, and acquisition information. - Participant-specific _sessions.json files describe the session-level variables. - EEG recordings and associated metadata are contained within participant/session directories.

De-identification

Acquisition dates have been de-identified for public release. Dates were altered while preserving the exact intervals between sessions within each participant. Therefore, the absolute acquisition dates should not be interpreted as the original dates of data collection.

Contributing Studies

The recordings were collected as part of multiple research projects. The dataset combines these recordings into a common BIDS-formatted dataset for public reuse. The contributing project identifiers used during data curation are not treated as separate source datasets in this release.

Missing Data

Missing demographic or clinical values are represented as n/a in accordance with BIDS conventions.

License

This dataset is released under the Creative Commons CC0 1.0 Universal dedication.

Contact

For questions regarding the dataset, please contact:

Nandakumar Narayanan nandakumar-narayanan@uiowa.edu University of Iowa

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=312, range 40–92 yr, mean 68.6 yr)

4045505560657075808590

Female · 120Male · 192

Sex composition

Female
120
Male
192
F : M ratio
0.62 : 1

38% female · n = 312 subjects with reported sex.

Channel counts (ch)

636466

Sampling frequencies (Hz)

50025000

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 63 (187), 64 (170), 66 ch · EEG · 500 Hz · mixed · 312 subjects, 358 recordings

Live trace viewer — sub-72 · ses-01 · task-rest

Showing one representative recording out of 312 subjects and 358 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 · 63 sensors — 63 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.ON008768(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON008768

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

class eegdash.dataset.ON008768(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Resting-State EEG in Parkinson’s Disease and Healthy Controls

Study:: on008768 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: ON008768.

Modality: eeg; Subject type: Unknown. Subjects: 312; recordings: 358; 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/on008768 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on008768 DOI: https://doi.org/10.82901/nemar.on008768

Examples

>>> from eegdash.dataset import ON008768
>>> dataset = ON008768(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 — ON008768.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 on008768 to reproduce the tutorial on this dataset.

Citation

Brooke Yeager, Arturo Espinoza, Rachel Cole, Christina Weber, Ergun Uc, … (n.d.). Resting-State EEG in Parkinson's Disease and Healthy Controls. 10.82901/nemar.on008768

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on008768.

Related & sibling datasets

ON007137EEG · 294 subj ON007096EEG · 292 subj ON007139EEG · 292 subj ON007052EEG · 288 subj ON007056EEG · 286 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.11.1

Sidecars

channels · eeg.json

Provenance

CC0 · 10.82901/nemar.on008768

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON008768`
Title	Resting-State EEG in Parkinson’s Disease and Healthy Controls
Author (year)	—
Canonical	—
Importable as	`ON008768`
Year	—
Authors	Brooke Yeager, Arturo Espinoza, Rachel Cole, Christina Weber, Ergun Uc, James Cavanagh, Nandakumar Narayanan
License	CC0
Citation / DOI	10.82901/nemar.on008768
Source links	OpenNeuro \| NeMAR \| Source URL

ON008768: eeg dataset, 312 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#