DS003509: eeg dataset, 56 subjects#

EEG: Simon Conflict in Parkinson’s

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh, Arun Singh, Kumar Narayanan (2021). EEG: Simon Conflict in Parkinson’s. 10.18112/openneuro.ds003509.v1.1.0

Modality: eeg Subjects: 56 Recordings: 84 License: CC0 Source: openneuro Citations: 5.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003509

dataset = DS003509(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 = DS003509(cache_dir="./data", subject="01")

Advanced query

dataset = DS003509(
    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{ds003509,
  title = {EEG: Simon Conflict in Parkinson's},
  author = {James F Cavanagh and Arun Singh and Kumar Narayanan},
  doi = {10.18112/openneuro.ds003509.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003509.v1.1.0},
}

About This Dataset#

Simon conflict task with cost of conflict reinforcement manipulation. 28 Parkinson patients and 28 matched controls. Task adapted from here: 10.1038/ncomms6394. Beh data first published here: 10.1016/j.cortex.2017.02.021. EEG published here: 10.1016/j.neuropsychologia.2018.05.020. PD came in twice separated by a week, either ON or OFF medication. CTL only came in once. Task included in Matlab programming language. Data collected circa 2015 in Cognitive Rhythms and Computation Lab at University of New Mexico. Subjs also had an acceleromter taped to their most tremor affected hand. X, Y, Z dimensions recorded throughout. Check the .xls sheet under code folder for more meta data. Triggers are complicated. See CC_Triggers.mat under code folder. Many analysis scripts are included; no idea how these hold up. Many are old. - James F Cavanagh 02/08/2021

Dataset Information#

Dataset ID	`DS003509`
Title	EEG: Simon Conflict in Parkinson’s
Author (year)	`Cavanagh2021_Simon`
Canonical	—
Importable as	`DS003509`, `Cavanagh2021_Simon`
Year	2021
Authors	James F Cavanagh, Arun Singh, Kumar Narayanan
License	CC0
Citation / DOI	10.18112/openneuro.ds003509.v1.1.0
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: 56
Recordings: 84
Tasks: 1

Channels & sampling rate

Channels: 67
Sampling rate (Hz): 500.0
Duration (hours): 48.53486111111111

Electrode Layout#

Electrode layout — EEG · 64 sensors — 64 channels

Dataset Statistics#

Age distribution (n=56, range 48–84 yr)

4550556065707580

Sex distribution

22

34

Female Male Total: 56

Channel counts: 67 ch (n=84 recordings)

Sampling frequencies: 500.0 Hz (n=84 recordings)

Total recording duration: 48 h

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

Signal Preview#

Live trace viewer — sub-021 · ses-02 · task-SimonConflict

Showing one representative recording out of 56 subjects and 84 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.

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS003509 class to access this dataset programmatically.

class eegdash.dataset.DS003509(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EEG: Simon Conflict in Parkinson’s

Study:: ds003509 (OpenNeuro)
Author (year):: Cavanagh2021_Simon
Canonical:: —

Also importable as: DS003509, Cavanagh2021_Simon.

Modality: eeg. Subjects: 56; recordings: 84; 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/ds003509 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003509 DOI: https://doi.org/10.18112/openneuro.ds003509.v1.1.0 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS003509
>>> dataset = DS003509(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)