EEGdash›OpenNeuro›DS007526
Iss. 7526 · 144 subjects · 277 recordings · CC0
Dataset Brief · PD-EEG

DS007526: eeg dataset, 144 subjects#

PD-EEG: Resting-State & Walking EEG in Parkinson’s Disease

Citation: Zoya Katzir, Daniel Vered, Inbal Maidan (inbalm@tlvmc.gov.il) (—). PD-EEG: Resting-State & Walking EEG in Parkinson’s Disease. 10.18112/openneuro.ds007526.v1.0.1

144-participant EEG dataset — PD-EEG: Resting-State & Walking EEG in Parkinson's Disease.

Data & curation Zoya Katzir · Daniel Vered · Inbal Maidan (inbalm@tlvmc.gov.il)
EEG · 65 ch250 HzBIDS 1.10.02 tasksParkinson'sMotorClinical/Intervention
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007526

dataset = DS007526(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007526(cache_dir="./data", subject="01")

Advanced query

dataset = DS007526(
    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{ds007526,
  title = {PD-EEG: Resting-State & Walking EEG in Parkinson's Disease},
  author = {Zoya Katzir and Daniel Vered and Inbal Maidan (inbalm@tlvmc.gov.il)},
  doi = {10.18112/openneuro.ds007526.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007526.v1.0.1},
}
§ 02Study · The README

About This Dataset#

This dataset contains EEG recordings from Parkinson’s disease (PD) patients and healthy controls (HC), collected under two behavioral conditions: resting state (sitting) and walking. The dataset was acquired at the Neurology Institute, Tel Aviv Sourasky Medical Center.

All participants provided informed consent. The study was approved by the local ethics committee and conducted in accordance with the Declaration of Helsinki.

PD-EEG: Resting-State & Walking EEG in Parkinson’s Disease

Overview

Experimental Design

Each participant underwent EEG recording under two conditions: 1. Resting State (144 Recordings)

  • Sitting

View full README

PD-EEG: Resting-State & Walking EEG in Parkinson’s Disease

Overview

Experimental Design

Each participant underwent EEG recording under two conditions: 1. Resting State (144 Recordings)

  • Sitting

  • Eyes open

  • Duration: ~4 minutes

  1. Walking (133 Recordings) - Walking on a treadmill at a comfortable speed while holding the handrails. - Duration: ~4 minutes

Additional clinical data were collected, including: - Demographic data - LEDD (Levodopa Equivalent Daily Dose) - a measure of anti-parkinsonian medication dosage. - MoCA (Montreal Cognitive Assessment) - a global measure of cognitive function. - MDS-UPDRS - Movement Disorder Society Unified Parkinson’s Disease Rating Scale - the gold standard clinical rating scale for Parkinson’s Disease. - CTT - Color Trails Test - a measure of executive function and processing speed.

EEG Acquisition

  • System: 64-channel Geodesic EEG System 400 (EGI system)

- Montage: International 10–20 system

Data Organization

This dataset follows the Brain Imaging Data Structure (BIDS) specification.

Typical structure:

sub-001/
    eeg/
        sub-001_task-rest_eeg.\*
        sub-001_task-walk_eeg.\*

participants.tsv
participants.json
dataset_description.json

Inbal Maidan, PhD Tel Aviv Sourasky Medical Center Email: inbalm@tlvmc.gov.il Daniel Vered, BSc Tel Aviv Sourasky Medical Center Email: vereddan@tlvmc.gov.il

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=144, range 39–84 yr, mean 65.1 yr)

35404550556065707580
Female · 59Male · 85

Sex composition

144
subjects
Female
59
Male
85
F : M ratio
0.69 : 1
41% female · n = 144 subjects with reported sex.

Channel counts: 65 ch (n=277 recordings)

Sampling frequencies: 250.0 Hz (n=277 recordings)

Total recording duration: 19 h 29 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 65 ch · EEG · 250 Hz · 144 subjects, 277 recordings
Live trace viewer — sub-021 · task-rest

Showing one representative recording out of 144 subjects and 277 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 · 64 sensors — 64 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.

NEMAR pipeline statistics — DS007526
HED event descriptors word cloud HED event descriptors word cloud — DS007526
§ 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…
Full dataset metadata table

Dataset ID

DS007526

Title

PD-EEG: Resting-State & Walking EEG in Parkinson’s Disease

Author (year)

Katzir2026

Canonical

—

Importable as

DS007526, Katzir2026

Year

—

Authors

Zoya Katzir, Daniel Vered, Inbal Maidan (inbalm@tlvmc.gov.il)

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007526.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007526,
  title = {PD-EEG: Resting-State & Walking EEG in Parkinson's Disease},
  author = {Zoya Katzir and Daniel Vered and Inbal Maidan (inbalm@tlvmc.gov.il)},
  doi = {10.18112/openneuro.ds007526.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007526.v1.0.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS007526(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Katzir2026
Canonical—
Importable asDS007526 · Katzir2026
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS007526(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

PD-EEG: Resting-State & Walking EEG in Parkinson’s Disease

Study:

ds007526 (OpenNeuro)

Author (year):

Katzir2026

Canonical:

—

Also importable as: DS007526, Katzir2026.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. Subjects: 144; recordings: 277; tasks: 2.

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/ds007526 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007526 DOI: https://doi.org/10.18112/openneuro.ds007526.v1.0.1

Examples

>>> from eegdash.dataset import DS007526
>>> dataset = DS007526(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 — DS007526.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 ds007526 to reproduce the tutorial on this dataset.

Citation

Zoya Katzir, Daniel Vered, Inbal Maidan (inbalm@tlvmc.gov.il) (n.d.). PD-EEG: Resting-State & Walking EEG in Parkinson's Disease. 10.18112/openneuro.ds007526.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds007526.v1.0.1.

BIDS
BIDS 1.10.0
Sidecars
events · events.json · channels · eeg.json
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#