EEGdash›NeMAR›ON002778

Iss. 2778 · 31 subjects · 46 recordings · CC0

Dataset Brief · UC San Diego Resting State EEG Data from Patients with Parkin…

ON002778: eeg dataset, 31 subjects#

UC San Diego Resting State EEG Data from Patients with Parkinson’s Disease

Citation: Alexander P. Rockhill, Nicko Jackson, Jobi George, Adam Aron, Nicole C. Swann (2019). UC San Diego Resting State EEG Data from Patients with Parkinson’s Disease. 10.82901/nemar.on002778

31-participant EEG dataset — UC San Diego Resting State EEG Data from Patients with Parkinson's Disease.

Data & curation Alexander P. Rockhill · Nicko Jackson · Jobi George · Adam Aron · Nicole C. Swann
Year 2019 · Distributed via NeMAR

EEG · 41 ch512 HzBIDS 1.11.1Task · rest3 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 ON002778

dataset = ON002778(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 = ON002778(cache_dir="./data", subject="01")

Advanced query

dataset = ON002778(
    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{on002778,
  title = {UC San Diego Resting State EEG Data from Patients with Parkinson's Disease},
  author = {Alexander P. Rockhill and Nicko Jackson and Jobi George and Adam Aron and Nicole C. Swann},
  doi = {10.82901/nemar.on002778},
  url = {https://doi.org/10.82901/nemar.on002778},
}

§ 02Study · The README

About This Dataset#

Welcome to the resting state EEG dataset collected at the University of San Diego and curated by Alex Rockhill at the University of Oregon.

Please email arockhil@uoregon.edu before submitting a manuscript to be published in a peer-reviewed journal using this data, we wish to ensure that the data to be analyzed and interpreted with scientific integrity so as not to mislead the public about findings that may have clinical relevance. The purpose of this is to be responsible stewards of the data without an “available upon reasonable request” clause that we feel doesn’t fully represent the open-source, reproducible ethos. The data is freely available to download so we cannot stop your publication if we don’t support your methods and interpretation of findings, however, in being good data stewards, we would like to offer suggestions in the pre-publication stage so as to reduce conflict in published scientific literature. As far as credit, there is precedent for receiving a mention in the acknowledgements section for reading and providing feedback on the paper or, for more involved consulting, being included as an author may be warranted. The purpose of asking for this is not to inflate our number of authorships; we take ethical considerations of the best way to handle intellectual property in the form of manuscripts very seriously, and, again, sharing is at the discretion of the author although we strongly recommend it. Please be ethical and considerate in your use of this data and all open-source data and be sure to credit authors by citing them.

An example of an analysis that we could consider problematic and would strongly advice to be corrected before submission to a publication would be using machine learning to classify Parkinson’s patients from healthy controls using this dataset. This is because there are far too few patients for proper statistics. Parkinson’s disease presents heterogeneously across patients, and, with a proper test-training split, there would be fewer than 8 patients in the testing set. Statistics on 8 or fewer patients for such a complicated diease would be inaccurate due to having too small of a sample size. Furthermore, if multiple machine learning algorithms were desired to be tested, a third split would be required to choose the best method, further lowering the number of patients in the testing set. We strongly advise against using any such approach because it would mislead patients and people who are interested in knowing if they have Parkinson’s disease.

DOI

Note that UPDRS rating scales were collected by laboratory personnel who had completed online training and not a board-certified neurologist. Results should be interpreted accordingly, especially that analyses based largely on these ratings should be taken with the appropriate amount of uncertainty. In addition to contacting the aforementioned email, please cite the following papers:

Nicko Jackson, Scott R. Cole, Bradley Voytek, Nicole C. Swann. Characteristics of Waveform Shape in Parkinson’s Disease Detected with Scalp Electroencephalography. eNeuro 20 May 2019, 6 (3) ENEURO.0151-19.2019; DOI: 10.1523/ENEURO.0151-19.2019.

Swann NC, de Hemptinne C, Aron AR, Ostrem JL, Knight RT, Starr PA. Elevated synchrony in Parkinson disease detected with electroencephalography. Ann Neurol. 2015 Nov;78(5):742-50. doi: 10.1002/ana.24507. Epub 2015 Sep 2. PMID: 26290353; PMCID: PMC4623949. George JS, Strunk J, Mak-McCully R, Houser M, Poizner H, Aron AR. Dopaminergic therapy in Parkinson’s disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control. Neuroimage Clin. 2013 Aug 8;3:261-70. doi: 10.1016/j.nicl.2013.07.013. PMID: 24273711; PMCID: PMC3814961.

View full README

DOI

Note that UPDRS rating scales were collected by laboratory personnel who had completed online training and not a board-certified neurologist. Results should be interpreted accordingly, especially that analyses based largely on these ratings should be taken with the appropriate amount of uncertainty. In addition to contacting the aforementioned email, please cite the following papers:

Nicko Jackson, Scott R. Cole, Bradley Voytek, Nicole C. Swann. Characteristics of Waveform Shape in Parkinson’s Disease Detected with Scalp Electroencephalography. eNeuro 20 May 2019, 6 (3) ENEURO.0151-19.2019; DOI: 10.1523/ENEURO.0151-19.2019.

Swann NC, de Hemptinne C, Aron AR, Ostrem JL, Knight RT, Starr PA. Elevated synchrony in Parkinson disease detected with electroencephalography. Ann Neurol. 2015 Nov;78(5):742-50. doi: 10.1002/ana.24507. Epub 2015 Sep 2. PMID: 26290353; PMCID: PMC4623949. George JS, Strunk J, Mak-McCully R, Houser M, Poizner H, Aron AR. Dopaminergic therapy in Parkinson’s disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control. Neuroimage Clin. 2013 Aug 8;3:261-70. doi: 10.1016/j.nicl.2013.07.013. PMID: 24273711; PMCID: PMC3814961.

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8.

Note: see this discussion on the structure of the json files that is sufficient but not optimal and will hopefully be changed in future versions of BIDS: https://neurostars.org/t/behavior-metadata-without-tsv-event-data-related-to-a-neuroimaging-data/6768/25.

NEMAR curation changes (2026-05-21, revised 2026-05-27)

The BIDS validator went from 4 errors + 1290 warnings to 0 errors + 968 warnings. None of the raw .bdf recordings were touched — every change is to a text sidecar. Participant table (``participants.tsv``) - The gender column held lowercase f/m, but the paired participants.json defines this column with uppercase categories (F = female, M = male). The 31 cells were capitalized to match so they read as valid values. - The hand column held lowercase r for the same reason — participants.json defines handedness as uppercase R/L/A — so it was capitalized to R across all 31 rows. - No other column values were changed.

Participant descriptions (``participants.json``) - The MMSE entry listed score bins (">24", "19 - 23", "10 - 18", "<9") as if they were the allowed cell values, which made the validator reject the actual integer scores (e.g. 30). It was rewritten as a plain numeric column measured in points, and the bin information was moved into the column’s description text where it belongs.

Dataset description (``dataset_description.json``) - Added DatasetType: "raw" so the dataset is validated as raw data rather than a derivative. - Updated BIDSVersion from 1.2.2 to 1.11.1, the version the current validator checks against. - Left GeneratedBy absent, as the source published it. The original OpenNeuro dataset declares no generation tooling, and this rehost only fixes validator issues, so nothing was invented here.

Task sidecar added at the dataset root (``task-rest_eeg.json``) - A new shared sidecar carrying a one-sentence task description paraphrased from this README (resting-state EEG; healthy controls have a single session, Parkinson’s patients two — one on and one off dopaminergic medication). Placed at the root so it applies to every recording at once instead of being repeated in each file.

Behavioral sidecar (``task-rest_beh.json``) - Added a task name (rest) and a task description noting this is resting state with no behavioral responses; the _beh.tsv files are placeholders kept only for BIDS compatibility, as the existing trial column note already explains. That original column documentation is preserved unchanged.

Events sidecar added at the dataset root (``task-rest_events.json``) - A new shared sidecar describing the four columns in every events table: onset and duration (both in seconds), sample, and value. The value column carries the BioSemi Status-channel trigger code. These columns appeared in the data but were previously undocumented; one root sidecar documents them for all recordings.

Recording sidecars (``_eeg.json``, all 46 recordings) - The misc-channel-count field was spelled MiscChannelCount; BIDS uses all-uppercase MISCChannelCount. It was renamed so the validator recognizes it. The value, 0, was already correct and is unchanged.

Acquisition times (``scans.tsv``) — left exactly as published - EEGDash’s loader appends a .000000 microsecond suffix to the acquisition times when it reads the files, but the published timestamps (e.g. 2011-01-19T11:22:56) are already valid BIDS — fractional seconds are optional — so they were left unchanged rather than having the loader’s suffix baked in.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=31, range 47–82 yr, mean 63.4 yr)

4550556065707580

Other · 31

Sex composition

Female
17
Male
14
F : M ratio
1.21 : 1

55% female · n = 31 subjects with reported sex.

HandednessRight · 31

Channel counts: 41 ch (n=46 recordings)

Sampling frequencies: 512.0 Hz (n=46 recordings)

Total recording duration: 2 h 31 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 41 ch · EEG · 512 Hz · 31 subjects, 46 recordings

Live trace viewer — sub-hc1 · ses-hc · task-rest

Showing one representative recording out of 31 subjects and 46 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 · 32 sensors — 32 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.ON002778(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON002778

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

class eegdash.dataset.ON002778(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

UC San Diego Resting State EEG Data from Patients with Parkinson’s Disease

Study:: on002778 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: ON002778, nan.

Modality: eeg. Subjects: 31; recordings: 46; 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/on002778 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on002778 DOI: https://doi.org/10.82901/nemar.on002778

Examples

>>> from eegdash.dataset import ON002778
>>> dataset = ON002778(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 — ON002778.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 on002778 to reproduce the tutorial on this dataset.

Citation

Alexander P. Rockhill, Nicko Jackson, Jobi George, Adam Aron, Nicole C. Swann (2019). UC San Diego Resting State EEG Data from Patients with Parkinson's Disease. 10.82901/nemar.on002778

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on002778.

Related & sibling datasets

NM000109EEG · 36 subj ON005420EEG · 37 subj ON007180EEG · 25 subj ON003343EEG · 20 subj NM000180EEG · 45 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.11.1

Sidecars

events · channels · eeg.json

Provenance

CC0 · 10.82901/nemar.on002778

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON002778`
Title	UC San Diego Resting State EEG Data from Patients with Parkinson’s Disease
Author (year)	—
Canonical	—
Importable as	`ON002778`
Year	2019
Authors	Alexander P. Rockhill, Nicko Jackson, Jobi George, Adam Aron, Nicole C. Swann
License	CC0
Citation / DOI	10.82901/nemar.on002778
Source links	OpenNeuro \| NeMAR \| Source URL

ON002778: eeg dataset, 31 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#